# Application of Machine Learning for Predicting Presence and Severity of Atherosclerotic Plaques in a Tertiary Care Hospital in Kashmir

Aasim Ayaz Wani, Fatima Abeer, Anjum Farhana, Ali Ahraz Wani, Kunnal Singh, Parvaiz Ahmad

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Aasim Ayaz Wani, Fatima Abeer, Anjum Farhana, Ali Ahraz Wani, Kunnal Singh, et al.. Application of Machine Learning for Predicting Presence and Severity of Atherosclerotic Plaques in a Tertiary Care Hospital in Kashmir. 2018. hal-05212303

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Distributed under a Creative Commons Attribution 4.0 International License
Application of Machine Learning for Predicting
Presence and Severity of Atherosclerotic Plaques
in a Tertiary Care Hospital in Kashmir

Aasim Ayaz Wani¹*†, Fatima Abeer²†, Anjum Farhana³†,
Ali Ahraz Wani⁴†, Kunnal Singh¹†, Parvaiz Ahmad Zargar²†

¹Department of Chemical Engineering, National Institute of Technology, Srinagar, Jammu & Kashmir, India, 190006.

²Department of Medicine, Government Medical College, Srinagar,
Jammu & Kashmir, India, 190010.

³Department of Microbiology, Government Medical College, Srinagar,
Jammu & Kashmir, India, 190010.

⁴Department of Medicine, University of Dhaka, Dhaka, Bangladesh, 1000.

*Corresponding author(s). E-mail(s): aasim.wani1@gmail.com;
Contributing authors: fatimaabeerm@gmail.com;
anjumfarhana1@yahoo.in; aliahraz03@gmail.com;
kunnalsingh1997@gmail.com;

†These authors contributed equally to this work.

Abstract

Many Cardiovascular diseases are driven by atherosclerosis and remain a leading global health challenge. This study employs advanced machine learning techniques to predict atherosclerotic plaque presence and arterial blockage severity, addressing the challenges of small, high-dimensional clinical datasets with missing values. Using methods like MICE, SMOTE, and PCA, we enhanced data quality, balanced class distributions, and improved model interpretability. Ensemble models, including Random Forest and XGBoost, achieved an AUC-ROC of 0.93 for plaque classification and demonstrated strong performance in blockage severity estimation. These findings highlight the potential for machine learning to improve early detection and personalized care in cardiovascular medicine. While limited by dataset size and scope, the study lays a foundation for scalable, interpretable predictive tools that align with clinical needs.
# 1 Introduction

## 1.1 Background

Cardiovascular diseases (CVDs) remain the leading global cause of mortality, claiming 17.9 million lives annually, constituting approximately 32% of all global deaths [1]. Heart attacks & strokes account for 85% of these, primarily driven by the insidious progression of atherosclerosis, often without noticeable symptoms [2]. Atherosclerosis, a chronic condition, involves the gradual accumulation of lipids, fibrous tissue, & inflammatory cells within arterial walls, ultimately leading to life-threatening events & a substantial economic burden on global healthcare systems [3]. Subclinical atherosclerosis, the early and often asymptomatic stage of the disease, is alarmingly prevalent. Epidemiological studies reveal that up to 50% of adults aged 40-59 & over 75% of those aged 60-79 exhibit arterial changes indicative of atherosclerosis [4]. This widespread presence of subclinical disease, however, often remains undetected until a catastrophic event, such as a myocardial infarction or stroke, occurs [5]. The United States, for example, witnesses approximately 805,000 heart attacks annually, with a concerning one-third representing recurrent events [6]. This highlights the critical need for early detection strategies to prevent the progression of the disease & improve patient outcomes. Current diagnostic approaches, including invasive imaging techniques & risk scoring systems, exhibit limitations in identifying individuals at high risk during the subclinical phase [7]. Overcoming these diagnostic challenges is paramount to enabling timely interventions & mitigating the significant economic burden associated with advanced-stage cardiovascular interventions [8].

## 1.2 Barriers to Early Atherosclerosis Detection

The barriers to early atherosclerosis detection are significant despite the progress made in diagnostic methods. Invasive techniques such as coronary angiography are recognized for their high accuracy but are impractical for routine screening due to their invasive nature, high costs, and limited availability, especially in resource-limited settings [7, 8]. Although non-invasive alternatives like computed tomography (CT) angiography are less invasive, they demand advanced infrastructure, including specialized scanners and trained personnel, along with substantial financial investment, thus limiting their use in large at-risk populations [9].

Traditional biochemical markers, such as lipid profiles, provide limited sensitivity for detecting early-stage atherosclerosis and are insufficient for accurately identifying high-risk individuals [10]. A significant limitation is their inability to capture the disease's complexity, evidenced by the fact that over 50% of individuals experiencing major cardiovascular events have LDL levels within clinically normal ranges [5].
Furthermore, conventional risk stratification models like the Framingham Risk Score oversimplify the relationships between risk factors by assuming linear interactions, thereby excluding emerging biomarkers that may have superior predictive capabilities [11].

The repercussions of delayed atherosclerosis detection are profound, often necessitating costly and invasive interventions such as stenting or bypass surgery, which significantly contribute to the economic burden of cardiovascular disease (CVD) [12]. In the United States alone, this cost is estimated at $216 billion annually. Therefore, the development of accessible and cost-effective tools for early atherosclerosis detection is imperative to reduce the prevalence of advanced disease, optimize healthcare resource allocation, and ultimately improve patient outcomes [2].

## 1.3 Challenges in Researching Asymptomatic Atherosclerosis

Research on asymptomatic atherosclerosis presents significant challenges due to the lack of overt clinical presentation, which limits opportunities for timely diagnosis and comprehensive data collection [13]. Subclinical or mild atherosclerosis often goes undetected unless it is identified incidentally during unrelated medical evaluations, such as imaging studies performed for other conditions [14]. For e.g asymptomatic plaques are sometimes detected during CT scans conducted for lung cancer screening or during carotid ultrasound evaluations performed for suspected stroke [15]. These incidental findings highlight the silent and often underdiagnosed nature of the disease but also underscore the difficulty of systematically identifying asymptomatic individuals outside of such contexts.

Routine screening for asymptomatic individuals is uncommon due to the high costs, invasiveness, and resource constraints associated with confirmatory diagnostic procedures [16]. Furthermore, the absence of overt symptoms in subclinical cases leads to delayed detection, often until the disease has progressed to an advanced stage or resulted in a catastrophic event, such as myocardial infarction or stroke [17]. As a result, datasets on asymptomatic atherosclerosis are often limited & biased toward symptomatic or high-risk populations, where data is more readily available [18]. This bias restricts the understanding of the early progression of atherosclerotic disease & complicates efforts to develop effective predictive models for broader populations [19]. Addressing these limitations will require innovative approaches that can handle sparse data, leverage incidental findings, & identify subtle, early-stage disease markers with high accuracy [20].

## 1.4 Focus and Scope of This Study

Subclinical atherosclerosis often progresses undiagnosed until catastrophic events such as myocardial infarction or stroke occur [21]. Routine screening for asymptomatic individuals is rare due to the high costs, invasiveness, and resource constraints of confirmatory diagnostic procedures [5, 8, 21]. Research on asymptomatic populations is further hindered by data sparsity, as diagnosis typically occurs incidentally during unrelated medical evaluations [21, 22]. Given these challenges, this study focuses on symptomatic individuals who present identifiable clinical signs of atherosclerosis. This
focus is practical, aligning with real-world clinical priorities where timely risk stratification and intervention can prevent disease progression and reduce recurrent events [8, 21]. However, this approach may limit the generalizability of findings to subclinical populations. Despite this limitation, the insights gained from studying symptomatic individuals can lay the groundwork for broader screening tools and methodologies [23]. By advancing predictive modeling in a clinically relevant population, this research contributes to bridging the gap in early detection and potentially informs future work aimed at subclinical disease detection [22].

## 1.5 Significance and Contribution of this Research

This research addresses two significant challenges in cardiovascular medicine and data science: the high dimensionality and frequent missing data often encountered in small atherosclerosis datasets. By focusing on symptomatic patients presenting with identifiable clinical indicators of atherosclerosis, this study aims to bridge diagnostic gaps [24]. Specifically, it seeks to identify which individuals require further confirmatory testing or specialist consultation, potentially leading to earlier interventions and improved outcomes [25, 26].

The study's tailored approach, focusing on symptomatic individuals, ensures that its findings have direct clinical applicability. Furthermore, the methodology employed in this research is broadly applicable to other medical domains that involve high-dimensional datasets with limited sample sizes. By offering practical insights into handling sparse and noisy data, this research has the potential to inform predictive modeling across diverse healthcare contexts, ultimately supporting personalized care and optimizing the allocation of healthcare resources.

## 1.6 Research Objectives

This study aims to develop and evaluate novel machine learning models for predicting atherosclerosis risk and severity in a challenging clinical setting: small, high-dimensional datasets with frequent missing values. Specifically, the research will focus on two key objectives (a) **Atherosclerosis Risk Prediction:** Develop and validate a predictive model to identify individuals with a high probability of having atherosclerotic plaques and (b) **Severity Estimation:** Develop and validate a model to estimate the maximum degree of arterial blockage in individuals diagnosed with atherosclerosis.

## 1.7 Structure of the Paper

The remainder of this paper is structured as follows: Section 2 reviews existing literature on atherosclerosis risk prediction and machine learning applications. Section 3 details the dataset and its inherent challenges, while Section 4 describes the methodology. Results and their implications are discussed in Sections 5 and 6, respectively, with conclusions and future directions provided in Section 7.
## 2 Literature Review

**The Clinical Burden of Atherosclerosis** Atherosclerosis continues to represent a principal driver of global morbidity and mortality, predominantly through its manifestations as cardiovascular diseases (CVDs), including ischemic heart disease, ischemic stroke, and peripheral arterial disease (PAD) [21]. The escalating prevalence of these conditions can be attributed to demographic shifts, such as aging populations, alongside lifestyle factors, particularly in low- and middle-income countries [27]. Extensive research, such as the findings of Lacey et al. [5], has established that early detection and timely intervention are pivotal in curtailing disease progression and mitigating the associated socioeconomic impact. However, constraints such as limited accessibility to healthcare resources in these regions hinder the implementation of preventative measures on a broad scale. Moreover, challenges in aligning population health strategies with the socio-economic realities of low-income countries further exacerbate these disparities [28]. The economic ramifications of atherosclerosis are equally profound, with healthcare systems facing substantial financial burdens from late-stage therapeutic interventions, including percutaneous coronary interventions and coronary artery bypass graft surgeries. While [29] highlighted the cost-saving potential of integrating ML for early detection, challenges remain in ensuring these models are cost-effective and scalable across diverse healthcare settings. For e.g., in resource-limited environments, infrastructural deficits may prevent the effective deployment of such technologies.

**Limitations of Traditional Diagnostic Approaches** Conventional diagnostic approaches, including coronary angiography, computed tomography (CT), and magnetic resonance imaging (MRI), have demonstrated significant efficacy in detecting advanced atherosclerotic lesions [30]. However, these methods are inherently resource-intensive, demanding specialized equipment and skilled personnel, thereby restricting their availability in resource-constrained environments [31]. Furthermore, their invasive nature and associated costs impede their routine use for screening asymptomatic populations [32]. Similarly, conventional biochemical markers, such as lipid profiles, fail to adequately encapsulate the intricate pathophysiology of atherosclerosis, leading to limitations in sensitivity and specificity for early-stage detection [33].

Emerging methodologies, including advanced molecular imaging and biomarker-driven diagnostics, exhibit considerable promise but remain largely confined to experimental settings. These limitations underscore the necessity for integrative diagnostic paradigms that synthesize imaging, clinical, and molecular data to yield a comprehensive understanding of atherosclerotic progression [15]. Predictive modeling frameworks that leverage such multifaceted datasets are poised to significantly enhance diagnostic precision while simultaneously mitigating reliance on invasive techniques [29]. Nonetheless, Cai et al. [34] caution that the lack of standardized protocols for integrating diverse datasets can result in inconsistent outcomes across clinical trials.

**Advances in Machine Learning for Atherosclerosis Prediction** The advent of machine learning (ML) has heralded a paradigm shift in the predictive modeling of atherosclerosis by harnessing high-dimensional datasets and uncovering non-linear interdependencies [35]. Ensemble methods, including Random Forest and XGBoost,
have demonstrated exceptional discriminatory capabilities, particularly when augmented with techniques such as synthetic minority oversampling (SMOTE) and principal component analysis (PCA) [36, 37]. Concurrently, explainable AI (XAI) frameworks have emerged as vital tools, enhancing interpretability and fostering clinician trust while maintaining predictive accuracy [38].

Numerous studies underscore the superior performance of ML-based models relative to traditional statistical methodologies. For instance, Hua et al. [39] demonstrated AUC-ROC values exceeding 0.9 for plaque classification tasks using ensemble methods, while Gallone et al. [40] highlighted how convolutional neural networks (CNNs) effectively analyze imaging data to detect subclinical atherosclerosis with heightened sensitivity and specificity. Additionally, recent advancements in multi-modal data fusion strategies have further amplified the predictive capacity of ML algorithms by integrating genomic, proteomic, and imaging information [41]. However, inconsistencies in model performance across different patient demographics, as noted by Hua et al. [34], highlight the critical need for robust validation in diverse populations.

**Challenges in High-Dimensional Cardiovascular Data** The analysis of high-dimensional clinical datasets presents myriad challenges, including incomplete data, class imbalance, and computational complexity [42, 43]. Strategies such as multiple imputation by chained equations (MICE) and dimensionality reduction techniques like PCA have been instrumental in mitigating these issues [20, 44]. Nonetheless, the effective integration of heterogeneous data modalities remains a formidable challenge, necessitating the development of advanced computational frameworks capable of preserving data integrity while enabling seamless integration [43].

Another significant obstacle lies in the interpretability of ML models within clinical contexts. While deep learning models often deliver superior predictive performance, their "black-box" nature poses a barrier to clinician adoption. Incorporating explainability into ML workflows is therefore essential for ensuring broader acceptance and utility in clinical practice [34, 38]. Additionally, the fusion of multi-modal data—encompassing imaging, clinical, and genomic domains—offers promising avenues for comprehensive analyses, yet requires sophisticated algorithms to ensure consistency and scalability [34]. For instance, the adaptation of these algorithms to smaller clinical datasets with missing or sparse features remains a critical bottleneck.

# 3 Methodology

## 3.1 Dataset Overview and Challenges

The dataset for this study was collected between 2016 and 2018 at the Outpatient Department of the Internal Medicine Cardiology Department, Government Medical College, Srinagar, a leading super-specialty hospital in Jammu and Kashmir. As a tertiary care center, the hospital caters to a varied patient population from urban and rural areas, offering a broad representation of cardiovascular health in the region. The dataset comprises records from 31 patients aged 40 to 79 years, encompassing 80 visits. Both male and female patients are represented, with a focus on menopausal status in female patients to evaluate gender-specific cardiovascular risk profiles. See Table 1 and Figure 1 regarding details about the density of various distributions.
<figure><img src="image_2.png" /><figcaption>**Fig. 1: Violin for Continuous Variables Stratified by Plaque Presence.** The figure shows the distribution of key continuous variables—Age, Cholesterol, Triglyceride, HDL, LDL, and Systolic Blood Pressure—grouped by the presence or absence of plaque (0: absence, 1: presence). Each boxplot illustrates the spread, median, and potential outliers for these variables. Differences in distributions highlight variations in these factors, such as elevated cholesterol and LDL levels in patients with plaque presence, which may indicate their association with atherosclerosis risk.</figcaption></figure>
<table><caption>**Table 1:** Paired Variable Summary Table showcasing 20 key variables from the dataset. Each variable's mean is presented along with its standard deviation (in parentheses), providing a detailed summary of central tendency and variability for continuous features.</caption><thead><tr><th>Variable 1</th><th>Mean (Std Dev)</th><th>Variable 2</th><th>Mean (Std Dev)</th></tr></thead><tbody><tr><td>Patient_ID</td><td>16.29 (9.3)</td><td>visit_number</td><td>2.38 (1.51)</td></tr><tr><td>Day</td><td>17.99 (8.31)</td><td>Month</td><td>6.80 (3.17)</td></tr><tr><td>Year</td><td>2016.62 (0.74)</td><td>Age</td><td>54.86 (9.46)</td></tr><tr><td>Sex(1: Male, 0:Female)</td><td>0.80 (0.4)</td><td>Smoking</td><td>1.00 (0.0)</td></tr><tr><td>Abnormal ECG</td><td>0.37 (1.62)</td><td>Ejection Fraction (ECO)</td><td>0.60 (0.33)</td></tr><tr><td>Plaque</td><td>0.72 (0.45)</td><td>(Left Marginal) LM</td><td>0.02 (0.14)</td></tr><tr><td>(Left Anterior Descending) LAD</td><td>0.36 (0.37)</td><td>(Left Circum Flex) LCX</td><td>0.22 (0.35)</td></tr><tr><td>(Right Coronary Artery) RCA</td><td>0.24 (0.33)</td><td>Cholestrol</td><td>149.32 (35.26)</td></tr><tr><td>Serum Urea</td><td>35.63 (9.14)</td><td>Serum Creatinine</td><td>1.21 (0.31)</td></tr><tr><td>Triglyceride</td><td>155.33 (78.07)</td><td>HDL</td><td>37.50 (9.06)</td></tr></tbody></table>

Containing 45 clinical, demographic, and biochemical variables, the dataset provides a robust foundation for predictive modeling of atherosclerosis risk as referenced in the Figure 2. These variables include lipid profiles, arterial blockages, and renal and hepatic markers, offering a comprehensive view of patient health. Data collection adhered to standardized clinical protocols, integrating biochemical assays, imaging diagnostics, and physical examinations to ensure consistency across observations. Additionally, temporal and seasonal variations were recorded to account for external factors influencing clinical markers, enhancing the dataset's reliability for research purposes.

Despite its strengths, the dataset presents several analytical challenges. (a) Missing data is a significant issue, with some variables having over 60% of observations missing as referenced in Figure 3. This missingness appears predominantly non-random, likely stemming from incomplete diagnostic follow-ups or equipment limitations, which complicates statistical analysis & model development. (b) Class imbalance is another challenge, with approximately 70% of observations indicating advanced-stage arterial blockages, while mild or subclinical cases are underrepresented. This imbalance, resulting from a selection bias favoring symptomatic patients, can skew predictive models & reduce their accuracy in detecting early-stage atherosclerosis. (c) Selection bias further limits the dataset, as its focus on symptomatic patients from a single center constrains its generalizability to broader populations & asymptomatic cases.

While these challenges restrict the dataset's immediate applicability to general populations, it remains a valuable resource for understanding atherosclerosis risk in a state. The data emphasizes clinical, demographic, & biochemical factors in high-risk populations, offering critical insights that can inform future studies and guide targeted interventions.
<figure><img src="image_3.png" /><figcaption>**Fig. 2:** Hierarchical representation of the dataset structure, categorizing variables into Patient Demographic, Cardiovascular, Clinical, Biochemical, Metabolic, and Longitudinal groups.</figcaption></figure>

## 3.2 Missing Value Imputation Methods

Handling missing data effectively is critical for preserving the integrity of analyses in high-dimensional datasets [45]. (Figure 3 highlights why imputation is necessary for this dataset). Singular imputation methods, including mean, median, and mode, offer computational simplicity by replacing missing values with a representative statistic [46]. While mean imputation maintains centrality, it can distort variability and relationships; median imputation is robust to outliers, ideal for skewed distributions; and mode imputation is suitable for categorical data but oversimplifies variability [47]. These methods, despite their efficiency, risk introducing bias and diminishing downstream model performance.

Building on simplicity, **K-Nearest Neighbor** (KNN) imputation incorporates local data relationships to estimate missing values [48]. It identifies *k*-nearest neighbors based on a distance metric, imputing the mean for continuous variables or the mode for categorical ones [49]. This method is particularly effective for small or sparse datasets, leveraging local patterns to capture complexity missed by singular approaches [48]. However, its computational cost, $O(n^2 \cdot d)$, limits scalability for large datasets, & its sensitivity to the choice of *k* & outliers requires parameter tuning [38].

**Gaussian Mixture Models (GMM)** offer a probabilistic alternative, modeling data as a mixture of Gaussian distributions and imputing missing values using the Expectation-Maximization (EM) algorithm [50]. This approach captures complex distributions & provides a framework for quantifying imputation uncertainty [51]. However, its assumptions about Gaussianity may not always hold, and the method's computational demands increase significantly with large, high-dimensional datasets [52].

**Multiple Imputation by Chained Equations (MICE)** extends flexibility by iteratively treating each variable with missing values as a dependent variable, using
others as predictors [53]. Through regression-based updates, MICE generates multiple imputed datasets to reflect plausible values and incorporate uncertainty [43]. It accommodates diverse data types and complex missing patterns, making it a robust choice for intricate datasets. While computationally intensive due to its iterative nature, MICE's ability to minimize bias and leverage all available data makes it a reliable method for high-dimensional imputation tasks [54].

<figure><img src="image_4.png" /><figcaption>Fig. 3: Percentage of Missing Values for Important Predictors. The plot highlights the proportion of missing data across key predictors identified as significant for plaque prediction.</figcaption></figure>

## 3.3 Dimensionality Reduction Method

Principal Component Analysis (PCA) is a dimensionality reduction technique that projects high-dimensional data into a lower-dimensional space while preserving maximum variance [55]. It identifies orthogonal principal components—linear combinations of original features—ranked by the variance they explain. The process involves standardizing data (mean = 0, variance = 1), computing the covariance matrix, and performing eigen decomposition to derive eigenvalues (variance explained) and eigenvectors (component directions) [56]. (See Figure 4 to understand how PCA performed
on this dataset). The top components explaining the desired variance (e.g., 95%) are
selected, and the data is projected onto them. PCA reduces noise, redundancy, and
computational costs, aiding visualization and improving machine learning performance
[57]. However, it assumes linearity, reduces interpretability, and requires careful pre-
processing, such as feature scaling. Despite these limitations, PCA is widely used for
simplifying high-dimensional data while retaining key information.

<figure><img src="image_5.png" /><figcaption>**Fig. 4:** The left panel shows the explained variance for the first 10 principal components, with cumulative variance (red) and an 80% threshold (green). The right panel presents the scatter plot of the data projected onto PCA1 and PCA2, highlighting the variance captured by these components.</figcaption></figure>

**3.4 Synthetic Minority Oversampling Technique (SMOTE)**

Class imbalance, where one class significantly outnumbers another often resulting in biased models that poorly predict the minority class [58]. The SMOTE mitigates this imbalance by generating synthetic samples for the minority class [59]. Unlike simple duplication, which risks overfitting, SMOTE creates new data points by interpolating between existing minority class instances [60]. This interpolation occurs along the line segment connecting a minority instance and one of its k-NN, with a random factor determining the degree of interpolation [61]. This ensures the synthetic samples are distributed across the feature space rather than clustering around existing points. By increasing the diversity and representation of the minority class, SMOTE improves the model's ability to learn its underlying patterns and enhances predictive performance [58]. It preserves feature relationships, avoiding the distortions introduced by duplication-based methods, making it particularly effective in imbalanced datasets [62]. However, SMOTE can introduce noise or overlap with the majority class if the minority and majority classes are poorly separated in the feature space. Excessive oversampling may also lead to overfitting, particularly in high-dimensional datasets where feature sparsity is common [63]. Its effectiveness is often enhanced when combined with preprocessing techniques and model optimization strategies, making it a cornerstone in addressing class imbalance.
## 3.5 Anomaly Detection Methods

Anomaly detection methods are indispensable for identifying deviations from expected patterns, especially in high-dimensional & sparse datasets. Among these, **Density-Based Spatial Clustering of Applications with Noise (DBSCAN)** is a density-based approach that identifies anomalies as points in low-density regions where no clusters are formed [51, 64]. It uses two parameters: *epsilon* (ε) to define the neighborhood radius & *minPts* to set the minimum number of points required for a dense region. Points are classified as core, border, or outliers, with the latter naturally falling into sparse regions [65]. DBSCAN is robust to noise and effective for datasets with non-uniform densities, but its performance can degrade in high-dimensional spaces due to the curse of dimensionality [64, 66].

While DBSCAN relies on density, **One-Class SVM (OC-SVM)** adopts a boundary-based approach, mapping data into a higher-dimensional space using kernel functions. It constructs a decision boundary around the majority of points, treating them as normal, with outliers falling outside this boundary [51, 67]. The *ν* parameter controls the trade-off between the model's complexity and the fraction of outliers allowed [68]. OC-SVM is particularly effective for high-dimensional, small-sample datasets where normal data dominates, but it requires careful tuning of kernel functions and parameters to avoid false positives or negatives [69, 70].

In contrast to density- and boundary-based methods, **Isolation Forest** isolates anomalies through recursive partitioning. Anomalies, being sparse and distant from dense regions, are typically isolated with fewer splits, resulting in shorter average path lengths across multiple trees [71, 72]. This tree-based approach is computationally efficient, scalable, and effective for large datasets, though its assumption that anomalies are sparse and distant may limit its effectiveness in more complex distributions [73, 74].

## 3.6 Prediction Task Methods

Accurate prediction of atherosclerosis risk requires leveraging machine learning techniques capable of addressing the inherent challenges of small, high-dimensional datasets. See Figure 5, to see the flowchart highlighting the methods followed in the paper. These datasets are often characterized by limited sample sizes, multicollinearity & substantial missing values, necessitating robust and adaptable predictive models [75, 75]. This study explores a range of algorithms tailored to these constraints, focusing on their effectiveness in extracting meaningful patterns and ensuring clinically relevant insights. (See Table 2 for details regarding some hyperparameter tuning of models).

**Linear regression**, while traditionally used for its simplicity and interpretability, encounters limitations in such settings due to its sensitivity to multicollinearity and overfitting in small datasets. To overcome these, advanced regularization techniques such as Ridge Regression, Lasso Regression, and Elastic Net have been employed in similar contexts [20, 46]. Ridge addresses multicollinearity by constraining coefficients, Lasso achieves feature selection through sparsity, & Elastic Net balances these
approaches, particularly excelling with correlated predictors [76, 77]. These methods have been shown to effectively identify critical biomarkers such as cholesterol & triglycerides, thereby improving the robustness & interpretability of atherosclerosis risk predictions [78].

<table><caption>**Table 2:** Summary of hyperparameter tuning results for various machine learning models used in the analysis. The table details the range of values tested for each hyperparameter and highlights the selected optimal values that achieved the best performance during model training and evaluation.</caption><thead><tr><th>Model and Hyperparameter</th><th>Range Tested (Optimal Value)</th></tr></thead><tbody><tr><td><strong>Random Forest</strong></td><td></td></tr><tr><td>Number of Trees</td><td>50, 100, 200, 500 (100)</td></tr><tr><td>Maximum Depth</td><td>None, 10, 20, 30 (20)</td></tr><tr><td>Minimum Samples Split</td><td>2, 5, 10 (5)</td></tr><tr><td>Minimum Samples Leaf</td><td>1, 2, 4 (2)</td></tr><tr><td><strong>XGBoost</strong></td><td></td></tr><tr><td>Learning Rate</td><td>0.01, 0.1, 0.2, 0.3 (0.1)</td></tr><tr><td>Number of Estimators</td><td>50, 100, 200 (100)</td></tr><tr><td>Maximum Depth</td><td>3, 5, 10 (5)</td></tr><tr><td>Subsample</td><td>0.6, 0.8, 1.0 (0.8)</td></tr><tr><td><strong>Support Vector Machine (SVM)</strong></td><td></td></tr><tr><td>Kernel</td><td>Linear, RBF (RBF)</td></tr><tr><td>Regularization Parameter (C)</td><td>0.1, 1, 10 (1)</td></tr><tr><td>Gamma</td><td>Scale, Auto, 0.1, 0.01 (Scale)</td></tr><tr><td><strong>Logistic Regression</strong></td><td></td></tr><tr><td>Regularization Type</td><td>L1, L2 (L2)</td></tr><tr><td>Regularization Parameter (C)</td><td>0.01, 0.1, 1, 10 (1)</td></tr><tr><td><strong>Gradient Boosting</strong></td><td></td></tr><tr><td>Learning Rate</td><td>0.01, 0.1, 0.2, 0.3 (0.1)</td></tr><tr><td>Number of Estimators</td><td>50, 100, 200 (100)</td></tr><tr><td>Maximum Depth</td><td>3, 5, 10 (3)</td></tr><tr><td><strong>K-Nearest Neighbors (KNN)</strong></td><td></td></tr><tr><td>Number of Neighbors</td><td>3, 5, 7, 9 (5)</td></tr><tr><td>Distance Metric</td><td>Euclidean, Manhattan (Euclidean)</td></tr><tr><td><strong>Naïve Bayes</strong></td><td></td></tr><tr><td>Smoothing Parameter</td><td>0.1, 0.5, 1.0 (0.5)</td></tr></tbody></table>

**Random Forest**, a widely adopted ensemble learning method, constructs numerous decision trees and aggregates their outputs, making it highly effective in handling complex, non-linear relationships [79]. Its randomized sampling of data and features reduces overfitting and enhances generalization, which is particularly advantageous for small, high-dimensional datasets [80]. Additionally, Random Forest provides feature importance scores, enabling the identification of key predictors—a critical requirement in clinical applications such as atherosclerosis risk prediction [81, 82].

**eXtreme Gradient Boosting (XGBoost)**, an advanced gradient boosting framework, addresses small, high-dimensional datasets through its regularization techniques (L1 & L2 penalties) & efficient parallelized implementation. It iteratively optimizes
an objective function, reducing errors from prior models while employing mecha-
nisms such as shrinkage & column sampling to enhance generalization performance.
XGBoost's ability to handle missing data & model complex patterns has made it a
powerful tool in predicting atherosclerosis risk and identifying critical biomarkers [37].

<figure><img src="image_6.png" /><figcaption>Fig. 5: Flowchart describing the process of data transformation and Modelling Process</figcaption></figure>

**3.7 Hybrid Preprocessing Approach**

To address the dual challenges of dimensionality and interpretability inherent in small,
high-dimensional medical datasets, this study implemented a hybrid preprocessing
strategy. This approach combined PCA for dimensionality reduction with the reten-
tion of clinically significant features identified through feature importance algorithms,
such as Random Forest, LIME, and SHAP. The hybrid strategy ensured that the pre-
processing pipeline reduced noise while preserving features critical for both predictive
accuracy and clinical relevance. Key features—such as LDL, HDL, age, diastolic blood
pressure, serum creatinine, and phosphate—were retained in their raw form based
on their high importance scores identified by feature selection methods. (See Figure
6 regarding the unequal distribution of data in this dataset). These scores reflected
the features' consistent contributions to model predictions, providing a robust basis
for their retention. Conversely, features with lower importance scores, often exhibit-
ing multicollinearity or high noise, were transformed into principal components to
minimize redundancy and enhance computational efficiency.

This hybrid approach was executed in the following steps: (a) Feature Importance
Analysis: Random Forest, LIME, and SHAP algorithms were employed to calculate
importance scores for all features in the dataset. These scores guided the identification
of clinically meaningful variables for retention in their raw form. (b) Dimensional-
ity Reduction: Features deemed less critical or prone to high multicollinearity were
transformed into a reduced set of principal components using PCA, capturing their col-
lective variability while mitigating noise. (c) Integration of Features: The dataset was
restructured by combining raw critical features with PCA-transformed components to
form the final hybrid dataset for modeling.

The hybrid preprocessing approach provided the following advantages: (a) Noise Reduction and Model Optimization: The PCA-transformed components effectively reduced noise and redundancy, improving the stability and efficiency of machine learning models. (b) Clinical Relevance and Interpretability: By retaining raw forms of key features with high clinical importance, the approach ensured interpretability aligned with domain expertise—a vital consideration for adoption in healthcare settings. (c) Scalability: The flexibility of combining algorithmic feature selection with PCA makes this preprocessing method adaptable to similar datasets across various domains.

However, the approach also posed certain challenges. The process of determining
which features to retain versus transform required careful tuning of feature impor-
tance thresholds and incorporation of domain knowledge. Additionally, integrating
PCA-transformed components with raw features created heterogeneous feature spaces
that could affect the behavior of certain algorithms, such as SVM. These limitations
suggest potential avenues for future refinement, including automating feature cate-
gorization or developing preprocessing pipelines that better balance dimensionality
reduction with interpretability. In summary, the hybrid preprocessing strategy demon-
strates the synergy between algorithmic feature selection and dimensionality reduction
techniques. This method provides a robust and interpretable framework for handling
high-dimensional medical datasets, particularly in contexts where small sample sizes
and clinical relevance are critical considerations.
<figure><img src="image_7.png" /><figcaption>**Fig. 6:** Distributions of selected clinical variables with highlighted low, normal, and high ranges. Each plot represents the frequency distribution of a specific variable with clinically relevant thresholds. The shaded regions indicate the low (yellow), normal (green), and high (red) ranges based on established clinical guidelines. Vertical dashed lines mark the exact thresholds, and annotations provide numeric values for clarity. These visualizations illustrate the spread and clinical categorization of key metrics, aiding in the identification of high-risk populations</figcaption></figure>
# 4 Discussion

## 4.1 Feature Importance Analysis and Implementation Details

The comparative analysis of feature importance derived from Random Forest, SHAP, and LIME provides valuable insights into the predictive power and interactions of individual features in the context of atherosclerosis risk assessment. The results in Figure 7 illustrate a consistent ranking of features across these methods, underscoring their robustness and alignment with clinical knowledge. This triangulated approach integrates the interpretability of SHAP and LIME with the model-driven importance scores from Random Forest, offering a comprehensive understanding of both global and local feature contributions [83]. See figure 8 which also gives some indication about the importance of variables. Also, see Figure 9 regarding details about the complex relation between subset of variables.

<figure><img src="image_8.png" /><figcaption>**Fig. 7:** Comparative analysis of feature importance derived from three methods: Random Forest, SHAP, and LIME The horizontal bar chart illustrates the top 10 predictors of plaque presence, ranked by their average importance across the methods. Age emerged as the most significant predictor, reflecting its strong correlation with the progression of atherosclerosis. Renal function markers (BUN, Serum Creatinine), lipid profile variables (LDL, HDL, Cholesterol), and systemic contributors such as Hemoglobin and Phosphate were also prominent, highlighting the multifactorial nature of plaque formation. The results emphasize the robustness of the feature selection process and its alignment with established clinical knowledge.</figcaption></figure>
**Age** consistently emerged as the most significant predictor, achieving the highest importance score across all methods. This finding aligns with established clinical evidence, as aging exacerbates arterial damage through cumulative oxidative stress, lipid deposition, and vascular stiffness [84]. The consistent importance of age across methods highlights its central role in both the machine learning model and traditional cardiovascular risk stratification frameworks [85].

Renal function markers, including **Blood Urea Nitrogen (BUN)** and **Serum Creatinine**, ranked prominently in the analysis, reflecting their critical role in the interplay between kidney dysfunction and cardiovascular health. Impaired renal function is known to contribute to systemic inflammation and endothelial dysfunction—key pathways in atherosclerosis progression [10]. The identification of these markers reinforces their relevance in predictive modeling, consistent with prior research linking renal impairment to heightened cardiovascular risk [86].

Lipid profile markers, such as **Low-Density Lipoprotein (LDL)**, **High-Density Lipoprotein (HDL)**, and **Cholesterol**, demonstrated strong predictive significance. Elevated LDL and total cholesterol are well-documented contributors to plaque formation through lipid accumulation and arterial inflammation, while HDL's protective role in reverse cholesterol transport underscores its inverse association with atherosclerosis risk [87]. Interestingly, the SHAP values revealed a nonlinear relationship between HDL and atherosclerosis risk, with diminishing protective effects at higher levels—a detail not readily captured by Random Forest.

Electrolyte and mineral imbalances, such as elevated **Phosphate** and **Sodium**, were also identified as important predictors [88]. Phosphate's association with vascular calcification and Sodium's role in blood pressure regulation emphasize the multifactorial nature of atherosclerosis, where systemic factors directly influence arterial wall stress and plaque progression [89]. These findings, particularly the interplay between mineral levels and other clinical markers, highlight the importance of accounting for interactions in predictive models [90].

**Hemoglobin (HB)** emerged as another significant predictor, reflecting the role of blood viscosity in vascular health. Elevated hemoglobin levels increase blood viscosity, imposing shear stress on arterial walls, which may accelerate plaque formation [91]. This finding aligns with broader systemic factors contributing to cardiovascular risk and suggests that integrating these markers enhances the model's clinical relevance [92].

While the consistency of feature rankings across methods emphasizes the dataset's reliability, some variability was observed for features with moderate importance, such as Serum Creatinine and LDL. SHAP and LIME provided additional insights into these features' local contributions, highlighting interactions with other variables that influence their predictive power. For example, SHAP values indicated that the effect of LDL on risk prediction was modulated by age, emphasizing the importance of incorporating feature interactions in machine learning models.
<figure><img src="image_9.png" /><figcaption>**Fig. 8:** This decision tree illustrates the classification of plaque presence using key clinical features such as age, cholesterol, triglycerides, HDL, LDL, systolic, and diastolic blood pressure. Nodes are color-coded (blue: "No Plaque," orange: "Plaque") with darker shades indicating higher class probabilities. Enhanced spacing and a grey background improve readability, while the tree's structure, limited to a depth of 7, balances interpretability and complexity. This visualization highlights the decision paths reflecting the relationships between features and plaque risk, offering an interpretable summary for clinical research.</figcaption></figure>
**Implementation Details.** Feature importance was evaluated using three complementary methods: Random Forest, SHAP, and LIME [81]. A Random Forest classifier was trained on a stratified dataset, split into 70% training and 30% testing subsets. Default hyperparameters, such as 100 trees and a maximum depth of 20, were used to mitigate overfitting given the small sample size. SHAP (SHapley Additive exPlanations) was applied to compute both global and local contributions, offering a detailed view of feature interactions and nonlinear effects [80]. LIME (Local Interpretable Model-agnostic Explanations) complemented this analysis by perturbing features for individual samples, elucidating local relationships that align with global patterns [93]. Results from all three methods were aggregated using an unweighted average, with visualizations presented in horizontal bar charts to facilitate comparative analysis.

Despite the robustness of this approach, challenges remain due to the high dimensionality and small sample size of the dataset. Multicollinearity among certain features and potential noise in predictors like Serum Creatinine underscore the need for advanced feature engineering [94]. Additionally, validating these findings on larger, diverse datasets is essential to ensure generalizability. Strategies such as transfer learning or data augmentation could further address these challenges.

In conclusion, this analysis highlights the centrality of age, lipid profiles, renal function markers, and systemic contributors in atherosclerosis risk prediction. The integration of SHAP, LIME, and Random Forest not only enhances interpretability but also uncovers important nonlinear relationships and interactions, underscoring the utility of explainable machine learning in clinical decision-making frameworks. Future efforts should focus on refining feature engineering, validating the approach across larger cohorts, and exploring longitudinal data to capture dynamic changes in predictors over time.

## 4.2 Imputation Techniques Evaluated and Broader Implications

Handling missing data in small, high-dimensional medical datasets poses significant challenges, particularly in the context of atherosclerosis risk prediction [95]. Missing values can obscure critical patterns, compromise model reliability, & lead to skewed interpretations of disease progression [96]. This study systematically evaluated a range of imputation techniques, spanning from simple statistical methods to machine learning approaches, to understand their impact on predictive accuracy & clinical relevance [97]. See Table 3 for more details regarding imputation model performance

Among the methods evaluated, MICE emerged as the most effective, consistently achieving the highest metrics across all performance evaluations. MICE iteratively models inter-variable relationships, ensuring realistic imputations that closely align the completed dataset with underlying clinical and biochemical patterns [43]. This capability is particularly valuable in high-dimensional datasets, where complex variable interactions—such as correlations between lipid profiles and cardiovascular outcomes—are critical for predictive modeling [98]. For example, MICE successfully reconstructed missing cholesterol and LDL values, preserving relationships with key
<figure><img src="image_10.png" /><figcaption>**Fig. 9: Correlation Heatmap of Clinical and Biochemical Parameters:** This heatmap visualizes the pairwise correlation coefficients between various clinical and biochemical variables, such as age, gender, cholesterol levels, and artery-specific features.</figcaption></figure>

<table><caption>**Table 3: Performance of Imputation Methods for Plaque Prediction**</caption><thead><tr><th>Method</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th><th>AUC-ROC</th></tr></thead><tbody><tr><td>Mean</td><td>0.76</td><td>0.78</td><td>0.74</td><td>0.76</td><td>0.82</td></tr><tr><td>Median</td><td>0.75</td><td>0.77</td><td>0.72</td><td>0.74</td><td>0.81</td></tr><tr><td>KNN</td><td>0.79</td><td>0.80</td><td>0.77</td><td>0.78</td><td>0.85</td></tr><tr><td>MICE</td><td><strong>0.81</strong></td><td><strong>0.83</strong></td><td><strong>0.79</strong></td><td><strong>0.81</strong></td><td><strong>0.87</strong></td></tr><tr><td>Mode</td><td>0.74</td><td>0.76</td><td>0.71</td><td>0.73</td><td>0.80</td></tr><tr><td>GMM</td><td>0.78</td><td>0.79</td><td>0.75</td><td>0.77</td><td>0.84</td></tr><tr><td>Regression</td><td>0.80</td><td>0.81</td><td>0.78</td><td>0.79</td><td>0.86</td></tr></tbody></table>

clinical markers such as plaque severity and age. While highly effective, MICE's iterative nature can lead to computational overhead, particularly for large datasets with numerous missing variables, necessitating careful consideration of its scalability [66].
Regression-based imputation methods also demonstrated strong performance, leveraging both local and global data patterns to maintain data integrity. In particular, *k*-NN excelled in datasets with pronounced local relationships, providing accurate imputations for features like serum urea and triglycerides. However, k-NN's computational intensity and sensitivity to feature scaling limit its scalability in high-dimensional spaces, where determining appropriate distance metrics becomes increasingly challenging. Simpler methods, such as mean, median, and mode imputation, underperformed as expected. These techniques fail to account for variable dependencies and often introduce biases, underscoring their inadequacy for complex medical datasets where preserving data integrity is essential.

The integration of robust imputation techniques like MICE was complemented by the use of SMOTE to address the dataset's significant class imbalance. SMOTE improved the representation of minority class observations, such as *Plaque* presence, by generating synthetic samples through interpolation between existing minority class instances [62]. This enhanced the sensitivity of predictive models, ensuring a more balanced analysis of critical cases. For instance, the recall for plaque detection increased significantly when SMOTE was applied alongside MICE-imputed datasets. However, SMOTE's reliance on existing data patterns underscores the importance of careful validation, as noise or outliers within the minority class can amplify biases in synthetic data. Exploring alternative synthetic data augmentation methods may further enhance data diversity while minimizing noise.

Beyond the immediate improvements in performance metrics, the broader implications of these preprocessing strategies are evident in their clinical relevance. Enhanced imputation ensures that machine learning models reflect the true variability of clinical datasets, reducing biases & improving generalizability to diverse patient populations. For example, the improved recall & AUC-ROC scores achieved through MICE and SMOTE highlight their potential to identify high-risk patients earlier & more reliably, supporting timely interventions that could prevent severe cardiovascular events. These strategies align with the overarching goal of leveraging small, high-dimensional datasets to provide actionable insights.

The study also underscores the importance of validating preprocessing pipelines to ensure reliability. Diagnostic visualizations, including learning curves and PCA scatter plots, confirmed the consistency and convergence of the selected techniques. For instance, PCA applied to the MICE-imputed dataset revealed a highly distributed variance structure, emphasizing the necessity of advanced imputation methods to minimize information loss. The integration of imputation and oversampling approaches effectively enhanced the dataset's completeness and balance, enabling predictive models to achieve robust performance in both plaque classification and broader clinical tasks.

Future research should extend these findings by incorporating artery-specific metrics and validating the impact of these techniques in larger, more diverse datasets. Additionally, advanced dimensionality reduction methods, such as t-SNE or UMAP, could be explored to complement MICE and SMOTE, particularly for capturing nonlinear relationships that PCA may overlook. Synthetic data augmentation techniques like GANs or Variational Autoencoders (VAEs) could further address challenges
related to data sparsity and diversity, enhancing the robustness of predictive mod-
els. Finally, longitudinal analyses could capture dynamic changes in clinical markers
over time, providing deeper insights into disease progression and improving the clini-
cal applicability of machine learning predictions. By addressing these challenges, this
study sets the stage for more reliable and scalable atherosclerosis risk prediction models
that align with the needs of clinical research and practice.

4.3 Analysis of Anomaly Detection Process

The visualization of the high-dimensional dataset after applying PCA offers valuable insights into the dataset's structure and the comparative performance of anomaly detection methods. PCA reduced the dataset to a lower-dimensional space, with the first two components capturing 31.79% of variance. (See Figure 4 regarding details about pca distribution). While this variance explains only a fraction of the dataset's complexity, it revealed clustering patterns and peripheral points that enabled anomaly detection algorithms to operate more effectively. This highlights the necessity of dimensionality reduction to mitigate the curse of dimensionality, especially in high-dimensional, small-sample datasets. However, PCA's reliance on linear transformations may limit its ability to capture nonlinear relationships inherent in clinical data, suggesting a potential role for nonlinear techniques like t-SNE or UMAP in future analyses. (See Figure 10 regarding details about performance of various models)

The **k-NN algorithm** effectively identified anomalies situated at the edges of the PCA-transformed feature space. By calculating the distance of each point to its nearest neighbors, k-NN flagged peripheral points as anomalies that deviated significantly from the central cluster. This robustness stems from k-NN's independence from assumptions about the underlying data distribution, making it particularly suited for heterogeneous datasets that combine biochemical markers and clinical metrics. Its ability to handle small datasets while detecting localized deviations underscores its reliability in this context.

**Isolation Forest**, which isolates anomalies through recursive partitioning of the feature space, exhibited behavior consistent with k-NN. Anomalies identified by Isolation Forest closely aligned with those flagged by k-NN, suggesting that the sparsity of the dataset amplified the visibility of peripheral points. Isolation Forest's independence from density-based assumptions ensures robust performance in sparse datasets, where traditional clustering techniques like DBSCAN may struggle [99]. Its reliance on tree-based structures makes it computationally efficient, particularly for high-dimensional data [22].

DBSCAN, a density-based algorithm, initially struggled with default hyperpa-
rameters, leading to most points being misclassified as anomalies. This emphasizes
the importance of careful parameter tuning in density-based methods [51]. By using
a k-NN distance plot to recalibrate the neighborhood size (eps), DBSCAN success-
fully identified a coherent central cluster and flagged peripheral points as anomalies
[51]. The recalibrated results closely matched those of k-NN and Isolation Forest,
demonstrating that precise parameter selection can significantly enhance DBSCAN's
effectiveness. However, DBSCAN's reliance on density assumptions may limit its
applicability in sparse, high-dimensional datasets.
<figure><img src="image_11.png" /><figcaption>**Fig. 10:** Anomaly detection results for the PCA-transformed MICE-imputed dataset with SMOTE balancing, using four different algorithms. (Top Left) k-Nearest Neighbors (k-NN) anomaly detection identifies anomalies based on distances to the k-th nearest neighbor, with distances shown in the color gradient. (Top Right) DBSCAN clusters data points and labels points as anomalies (red) if they lie in low-density regions. (Bottom Left) Isolation Forest identifies anomalies (red) by isolating points in fewer partitions relative to normal points (blue). (Bottom Right) One-Class SVM classifies points as anomalies (red) or normal (blue) using a hyperplane in the PCA-transformed feature space.</figcaption></figure>
The OC-SVM algorithm, which constructs a boundary around the majority of the data, flagged a larger number of peripheral points as anomalies compared to other methods. This stricter classification behavior results from OC-SVM's sensitivity to sparsity in high-dimensional spaces, often leading to higher false positive rates. Despite this limitation, OC-SVM successfully captured subtle deviations that other methods might have overlooked, highlighting its utility in identifying nuanced anomalies. However, parameter tuning remains crucial to minimize false positives and improve its robustness.

Overall, PCA preprocessing proved effective in reducing the dataset's complexity and enabling anomaly detection methods to focus on meaningful deviations. Across all methods, peripheral points in the PCA-transformed space were consistently flagged as anomalies, suggesting that these points may correspond to rare clinical or biochemical profiles. While PCA was instrumental in this study, its linear assumptions might limit its utility in datasets with complex nonlinear relationships, making the exploration of nonlinear techniques like t-SNE or UMAP a logical next step.

Focusing on flagged anomalies provides significant practical & clinical value. In clinical datasets, anomalies often correspond to rare conditions, extreme physiological states, or potential data errors. For instance, flagged anomalies might indicate patients with abnormal cholesterol or electrolyte levels, offering diagnostic or prognostic insights. Prioritizing these points enables a balance between extracting meaningful patterns & mitigating overfitting risks inherent in small datasets. Moreover, the consistent agreement among methods—k-NN, Isolation Forest, DBSCAN, & OC-SVM—reinforces confidence in the identified anomalies, establishing them as a focal point for subsequent clinical validation & exploration.

In conclusion, the analysis demonstrates that methods like k-NN and Isolation Forest are particularly robust for anomaly detection in small, high-dimensional datasets. In contrast, DBSCAN and OC-SVM require careful calibration to achieve optimal performance. Future work should explore alternative dimensionality reduction techniques, such as t-SNE or UMAP, to better capture nonlinear patterns. Additionally, ensemble approaches combining multiple anomaly detection methods could enhance robustness and interpretability, ensuring reliable insights for clinical and research applications.

## 4.4 Evaluating Machine Learning Models for Artery Blockage Risk Prediction

Accurately predicting the risk of artery blockage is critical for timely intervention and improved management of CVDs. This study evaluated the performance of several machine learning models on a dataset with significant challenges: high dimensionality, small sample size, missing values, and severe class imbalance. The dataset predominantly included symptomatic patients, leading to potential biases in model performance. Two preprocessing strategies, PCA and feature selection, were applied to mitigate these challenges and were compared for their impact on predictive accuracy, robustness, and interpretability. (See Table 4 for details)

**Performance on PCA-Transformed Data.** The PCA-transformed dataset consistently yielded superior performance compared to the feature-selected dataset
across all models. Logistic Regression was the best-performing model on the PCA-
transformed data, achieving an accuracy of 0.93 ± 0.04 and a near-perfect ROC AUC
of 0.98 ± 0.02. This performance underscores the utility of PCA in simplifying com-
plex feature spaces while retaining the most informative components, allowing linear
models to generalize effectively even with small datasets. Random Forest closely fol-
lowed with 0.92 ± 0.06 accuracy and 0.95 ± 0.05 ROC AUC, leveraging its ensemble
structure to capture non-linear feature interactions.

**Sensitivity and Precision Trade-offs.** Support Vector Machines (SVM) achieved perfect recall (1.00 ± 0.00), reflecting its strength in sensitivity-critical tasks where minimizing false negatives is essential. However, the precision (0.89 ± 0.03) was lower, indicating a tendency to overclassify positive cases. This behavior aligns with the inherent imbalance in the dataset, as models were likely biased toward symptomatic patients, the dominant class. Gradient Boosting demonstrated balanced performance with 0.88 ± 0.10 accuracy and 0.92 ± 0.06 F1 score, indicating robustness in handling noisy data but showing susceptibility to overfitting due to the dataset's size. Simpler models, such as k-Nearest Neighbors (k-NN) and Naïve Bayes, exhibited significantly weaker performance, with ROC AUC scores of 0.85 ± 0.05 and 0.85 ± 0.13, respectively. These models struggled to capture the complexity of the underlying feature relationships, particularly without the benefit of ensemble learning or advanced feature engineering.

**Comparing Ensemble Methods.** Ensemble methods, such as Random Forest and Gradient Boosting, showed notable differences in their ability to handle class imbalance. Random Forest's bagging approach, which trains multiple decision trees on bootstrap samples of the dataset, resulted in higher recall (0.83 ± 0.09) compared to Gradient Boosting (0.77 ± 0.06) on feature-selected datasets. This indicates Random Forest's capacity to detect more minority-class instances, a critical advantage when identifying high-risk patients in an imbalanced dataset. Gradient Boosting, on the other hand, excelled in precision and overall F1 score, suggesting that its boosting framework, which iteratively focuses on hard-to-classify examples, was more effective at refining decision boundaries and reducing false positives. These differences suggest that Random Forest may be better suited for scenarios where sensitivity is critical, whereas Gradient Boosting offers advantages in applications prioritizing balanced performance metrics.

**Performance on Raw Dataset with Feature Selection.** In contrast, models trained on the raw dataset with top selected features showed noticeable performance declines. Logistic Regression, which excelled on PCA-transformed data, experienced a sharp drop in ROC AUC to 0.31 ± 0.10, demonstrating its reliance on multicollinearity-free data for effective generalization. Ensemble methods retained moderate performance, but the exclusion of secondary features likely hindered their ability to model complex relationships. Random Forest achieved slightly better recall, while Gradient Boosting retained its edge in precision, reflecting the trade-offs in their respective learning frameworks.

**Dataset Challenges and Biases.** The dataset's structure, including significant missing values and imbalanced classes, played a pivotal role in shaping the observed results. The dominance of symptomatic cases likely contributed to the high sensitivity
observed in models such as SVM and Random Forest, as these patterns were well-represented during training. However, this imbalance also raises concerns about the broader applicability of these models to asymptomatic populations or those with early-stage disease. The small validation sets further compound this issue, as they limit the ability to reliably estimate model performance on diverse populations. For instance, while Random Forest achieved high recall, its performance could be inflated due to its tendency to overfit minority class patterns present in the imbalanced training data. Similarly, Gradient Boosting's superior precision might reflect its iterative focus on well-represented samples rather than true generalization across the full spectrum of patient profiles.

**Preprocessing Trade-offs.** PCA proved to be a critical preprocessing step, effectively addressing the curse of dimensionality and improving model robustness. By reducing redundancy and retaining meaningful variance, PCA enabled both linear and non-linear models to generalize more effectively. In contrast, feature selection prioritized interpretability at the cost of accuracy, as excluding secondary patterns diminished the models' ability to capture complex relationships. For clinical applications, particularly in scenarios requiring actionable insights, a hybrid approach combining PCA with selective inclusion of clinically significant features could provide a balance between performance and interpretability.

**Implications and Future Directions.** The results emphasize the necessity of external validation on larger, more balanced datasets. While the models performed well on the current dataset, their real-world applicability remains uncertain due to its focus on symptomatic patients from a single center. External validation, particularly using data from multiple centers or incorporating asymptomatic cases, is essential to assess the robustness and generalizability of these models. Furthermore, the integration of additional data modalities, such as imaging or longitudinal data, could enhance the models' ability to capture dynamic disease progression, mitigating biases and improving practical utility.

In conclusion, the study highlights the potential of PCA as a preprocessing technique for managing high-dimensional, imbalanced datasets. Logistic Regression and Random Forest emerged as robust performers, while Gradient Boosting demonstrated its strength in precision-focused tasks. SVM's high recall reinforces its utility for sensitivity-driven applications, though all models' reliance on a biased dataset underscores the need for external validation. Addressing these limitations will be crucial for translating machine learning models into effective tools for atherosclerosis risk prediction and management.
<table><caption>**Table 4:** Performance of Classification Models for PCA, Raw, and Hybrid Preprocessed Datasets from Balanced and Imbalanced Datasets for Predicting the Risk of Heart Blockage in Arteries.</caption><thead><tr><th>Model</th><th>Type</th><th>Balance</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th><th>ROC AUC</th></tr></thead><tbody><tr><td>Logistic Regression</td><td>PCA</td><td>No</td><td>0.93 ± 0.04</td><td>0.94 ± 0.05</td><td>0.97 ± 0.04</td><td>0.95 ± 0.03</td><td>0.98 ± 0.02</td></tr><tr><td>SVM</td><td>PCA</td><td>No</td><td>0.91 ± 0.03</td><td>0.89 ± 0.03</td><td>1.00 ± 0.00</td><td>0.94 ± 0.02</td><td>0.94 ± 0.04</td></tr><tr><td>Random Forest</td><td>PCA</td><td>No</td><td>0.92 ± 0.06</td><td>0.91 ± 0.05</td><td>0.98 ± 0.03</td><td>0.95 ± 0.04</td><td>0.95 ± 0.05</td></tr><tr><td>Gradient Boosting</td><td>PCA</td><td>No</td><td>0.88 ± 0.10</td><td>0.91 ± 0.08</td><td>0.93 ± 0.06</td><td>0.92 ± 0.06</td><td>0.91 ± 0.12</td></tr><tr><td>kNN</td><td>PCA</td><td>No</td><td>0.80 ± 0.06</td><td>0.85 ± 0.05</td><td>0.89 ± 0.08</td><td>0.87 ± 0.04</td><td>0.85 ± 0.05</td></tr><tr><td>Naïve Bayes</td><td>PCA</td><td>No</td><td>0.82 ± 0.12</td><td>0.86 ± 0.09</td><td>0.92 ± 0.10</td><td>0.88 ± 0.08</td><td>0.85 ± 0.13</td></tr><tr><td>Logistic Regression</td><td>Raw</td><td>No</td><td>0.65 ± 0.10</td><td>0.70 ± 0.04</td><td>0.91 ± 0.12</td><td>0.79 ± 0.07</td><td>0.31 ± 0.10</td></tr><tr><td>SVM</td><td>Raw</td><td>No</td><td>0.72 ± 0.02</td><td>0.72 ± 0.02</td><td>1.00 ± 0.00</td><td>0.84 ± 0.02</td><td>0.44 ± 0.19</td></tr><tr><td>Random Forest</td><td>Raw</td><td>No</td><td>0.66 ± 0.02</td><td>0.74 ± 0.05</td><td>0.83 ± 0.09</td><td>0.78 ± 0.02</td><td>0.64 ± 0.03</td></tr><tr><td>Gradient Boosting</td><td>Raw</td><td>No</td><td>0.65 ± 0.06</td><td>0.76 ± 0.08</td><td>0.77 ± 0.06</td><td>0.76 ± 0.04</td><td>0.64 ± 0.09</td></tr><tr><td>kNN</td><td>Raw</td><td>No</td><td>0.69 ± 0.08</td><td>0.75 ± 0.07</td><td>0.86 ± 0.06</td><td>0.80 ± 0.05</td><td>0.64 ± 0.09</td></tr><tr><td>Naïve Bayes</td><td>Raw</td><td>No</td><td>0.63 ± 0.08</td><td>0.72 ± 0.02</td><td>0.79 ± 0.19</td><td>0.74 ± 0.09</td><td>0.55 ± 0.13</td></tr><tr><td>Logistic Regression</td><td>PCA</td><td>Yes</td><td>0.68 ± 0.05</td><td>0.81 ± 0.05</td><td>0.54 ± 0.06</td><td>0.65 ± 0.05</td><td>0.73 ± 0.03</td></tr><tr><td>SVM</td><td>PCA</td><td>Yes</td><td>0.75 ± 0.04</td><td>0.77 ± 0.04</td><td>0.74 ± 0.05</td><td>0.75 ± 0.05</td><td>0.87 ± 0.04</td></tr><tr><td>Random Forest</td><td>PCA</td><td>Yes</td><td>0.85 ± 0.03</td><td>0.90 ± 0.02</td><td>0.80 ± 0.03</td><td>0.85 ± 0.03</td><td>0.92 ± 0.02</td></tr><tr><td>Gradient Boosting</td><td>PCA</td><td>Yes</td><td>0.79 ± 0.04</td><td>0.80 ± 0.04</td><td>0.78 ± 0.05</td><td>0.79 ± 0.05</td><td>0.89 ± 0.03</td></tr><tr><td>kNN</td><td>PCA</td><td>Yes</td><td>0.70 ± 0.06</td><td>0.88 ± 0.05</td><td>0.50 ± 0.07</td><td>0.64 ± 0.06</td><td>0.72 ± 0.04</td></tr><tr><td>Naïve Bayes</td><td>PCA</td><td>Yes</td><td>0.65 ± 0.05</td><td>0.76 ± 0.06</td><td>0.61 ± 0.07</td><td>0.68 ± 0.04</td><td>0.69 ± 0.03</td></tr><tr><td>Logistic Regression</td><td>Hybrid</td><td>Yes</td><td>0.72 ± 0.05</td><td>0.77 ± 0.12</td><td>0.70 ± 0.11</td><td>0.71 ± 0.05</td><td>0.79 ± 0.06</td></tr><tr><td>Random Forest</td><td>Hybrid</td><td>Yes</td><td>0.85 ± 0.05</td><td>0.93 ± 0.09</td><td>0.90 ± 0.08</td><td>0.88 ± 0.07</td><td>0.96 ± 0.03</td></tr><tr><td>SVM</td><td>Hybrid</td><td>Yes</td><td>0.40 ± 0.08</td><td>0.25 ± 0.14</td><td>0.28 ± 0.32</td><td>0.23 ± 0.19</td><td>0.41 ± 0.08</td></tr><tr><td>Gradient Boosting</td><td>Hybrid</td><td>Yes</td><td>0.86 ± 0.07</td><td>0.92 ± 0.09</td><td>0.77 ± 0.12</td><td>0.83 ± 0.09</td><td>0.93 ± 0.06</td></tr><tr><td>kNN</td><td>Hybrid</td><td>Yes</td><td>0.66 ± 0.11</td><td>0.77 ± 0.17</td><td>0.53 ± 0.09</td><td>0.61 ± 0.09</td><td>0.73 ± 0.09</td></tr><tr><td>Naïve Bayes</td><td>Hybrid</td><td>Yes</td><td>0.75 ± 0.07</td><td>0.82 ± 0.13</td><td>0.70 ± 0.12</td><td>0.74 ± 0.06</td><td>0.84 ± 0.04</td></tr></tbody></table>

## 4.5 Evaluating Machine Learning Models for Predicting Percentage Maximum Artery Blockage in Patients

This study utilized a dataset comprising clinical, biochemical, and demographic variables collected during cardiovascular evaluations to predict the percentage of maximum artery blockage in patients. The target variable, Max_Blockage, was derived from the maximum values observed across four artery-specific columns: Left Marginal (LM), Left Anterior Descending (LAD), Left Circum Flex (LCX), and Right Coronary Artery (RCA). To avoid data leakage, these artery-specific columns were excluded from the dataset after creating the target variable. Missing data were handled using the Iterative Imputer method, which iteratively estimates missing values as a function of other variables. This method is particularly suitable for the heterogeneity of clinical datasets, effectively capturing both linear and non-linear relationships.
**Preprocessing and Feature Engineering.** To address high dimensionality and improve interpretability, feature selection was performed using domain knowledge and feature importance analysis. The final feature set included 11 clinically significant variables, such as MCH, Age, Serum Creatinine, MCV, Calcium, Potassium, Sodium, HDL, Cholesterol, and Triglyceride. These variables were selected based on their relevance to cardiovascular risk, identified through clinical expertise and statistical methods like Random Forest feature importance. This process ensured that the selected features retained the most predictive and clinically meaningful variables while minimizing noise and redundancy.

Principal Component Analysis (PCA) was applied to the selected features to further reduce dimensionality by transforming them into orthogonal components. A 95% variance retention threshold was chosen to balance computational efficiency and the preservation of essential patterns. While PCA reduced redundancy and collinearity, it also risked obscuring clinically relevant relationships. These trade-offs between dimensionality reduction and feature interpretability were carefully evaluated in the study.

**Experimental Design.** Two feature sets were compared: the raw reduced features and the PCA-transformed features derived from them. The dataset was standardized using *MinMaxScaler*, which scaled values between 0 and 1, ensuring consistency across all features. Data was split into training and testing subsets, with 80% allocated for training and 20% for testing, using a fixed random seed for reproducibility. Performance was validated using a 5-fold cross-validation strategy, allowing each subset to serve as a validation set once. This robust design mitigated overfitting and provided reliable performance metrics.

**Regression Models and Metrics.** A variety of regression models were tested to evaluate their suitability for predicting the percentage of artery blockage. Linear models (Linear Regression, Ridge, Lasso, ElasticNet) provided simplicity and interpretability, making them foundational choices for regression tasks. Tree-based models (Random Forest, Gradient Boosting, Decision Tree, AdaBoost) captured non-linear relationships and complex feature interactions. Support Vector Machines (SVR) with both linear and RBF kernels were included for their robustness in high-dimensional spaces, while k-Nearest Neighbors (k-NN) served as a baseline instance-based learning method. Deep learning models were excluded due to the small dataset size (86 observations), which increased the risk of overfitting. Model performance was assessed using Mean Absolute Error (MAE), a robust metric suited for clinical applications due to its lower sensitivity to outliers compared to Mean Squared Error (MSE).

**Findings and Inferences.** Results revealed clear differences between models trained on raw features and those trained on PCA-transformed features (See Table 5 for details). While PCA reduced noise and redundancy, it also slightly diminished performance for certain models. For instance, Random Forest achieved an MAE of 0.27 ± 0.03 using raw features but saw a slight increase to 0.28 ± 0.04 with PCA-transformed features. Similarly, RMSE rose from 0.37 ± 0.04 to 0.39 ± 0.05. These results suggest that while PCA enhances computational efficiency, it can obscure subtle but clinically relevant patterns in the data, particularly for models that rely heavily on feature-specific interactions.
<table><caption>**Table 5:** Combined Cross-Validation Performance of Regression Models for PCA, Raw, and Hybrid Preprocessed Datasets from Balanced and Imbalanced Datasets for Predicting the Amount of Max Blockage in arteries.</caption><thead><tr><th>Algorithm</th><th>Scenario</th><th>Feature Type</th><th>MAE (95% CI)</th><th>RMSE (95% CI)</th></tr></thead><tbody><tr><td>Random Forest</td><td>Unbalanced</td><td>Raw</td><td>0.27 ± 0.03</td><td>0.37 ± 0.04</td></tr><tr><td>Random Forest</td><td>Unbalanced</td><td>PCA</td><td>0.28 ± 0.04</td><td>0.39 ± 0.05</td></tr><tr><td>Gradient Boosting</td><td>Unbalanced</td><td>Raw</td><td>0.29 ± 0.03</td><td>0.38 ± 0.05</td></tr><tr><td>Gradient Boosting</td><td>Unbalanced</td><td>PCA</td><td>0.31 ± 0.04</td><td>0.41 ± 0.06</td></tr><tr><td>Linear Regression</td><td>Unbalanced</td><td>Raw</td><td>0.14 ± 0.02</td><td>0.22 ± 0.03</td></tr><tr><td>Linear Regression</td><td>Unbalanced</td><td>PCA</td><td>0.18 ± 0.03</td><td>0.24 ± 0.03</td></tr><tr><td>Ridge Regression</td><td>Unbalanced</td><td>Raw</td><td>0.31 ± 0.04</td><td>0.37 ± 0.05</td></tr><tr><td>Ridge Regression</td><td>Unbalanced</td><td>PCA</td><td>0.33 ± 0.05</td><td>0.39 ± 0.06</td></tr><tr><td>Lasso Regression</td><td>Unbalanced</td><td>Raw</td><td>0.32 ± 0.05</td><td>0.36 ± 0.05</td></tr><tr><td>Lasso Regression</td><td>Unbalanced</td><td>PCA</td><td>0.34 ± 0.05</td><td>0.38 ± 0.06</td></tr><tr><td>Support Vector Regressor</td><td>Unbalanced</td><td>Raw</td><td>0.30 ± 0.04</td><td>0.40 ± 0.05</td></tr><tr><td>Support Vector Regressor</td><td>Unbalanced</td><td>PCA</td><td>0.31 ± 0.05</td><td>0.42 ± 0.06</td></tr><tr><td>Random Forest</td><td>Balanced</td><td>Raw</td><td>0.26 ± 0.03</td><td>0.31 ± 0.04</td></tr><tr><td>Random Forest</td><td>Balanced</td><td>PCA</td><td>0.32 ± 0.04</td><td>0.38 ± 0.05</td></tr><tr><td>Gradient Boosting</td><td>Balanced</td><td>Raw</td><td>0.28 ± 0.03</td><td>0.33 ± 0.04</td></tr><tr><td>Gradient Boosting</td><td>Balanced</td><td>PCA</td><td>0.34 ± 0.05</td><td>0.39 ± 0.06</td></tr><tr><td>Linear Regression</td><td>Balanced</td><td>Raw</td><td>0.15 ± 0.02</td><td>0.23 ± 0.03</td></tr><tr><td>Linear Regression</td><td>Balanced</td><td>PCA</td><td>0.19 ± 0.03</td><td>0.25 ± 0.03</td></tr><tr><td>Ridge Regression</td><td>Balanced</td><td>Raw</td><td>0.29 ± 0.03</td><td>0.35 ± 0.04</td></tr><tr><td>Ridge Regression</td><td>Balanced</td><td>PCA</td><td>0.31 ± 0.04</td><td>0.37 ± 0.05</td></tr><tr><td>Lasso Regression</td><td>Balanced</td><td>Raw</td><td>0.30 ± 0.04</td><td>0.34 ± 0.05</td></tr><tr><td>Lasso Regression</td><td>Balanced</td><td>PCA</td><td>0.33 ± 0.05</td><td>0.36 ± 0.06</td></tr><tr><td>Support Vector Regressor</td><td>Balanced</td><td>Raw</td><td>0.28 ± 0.03</td><td>0.35 ± 0.05</td></tr><tr><td>Support Vector Regressor</td><td>Balanced</td><td>PCA</td><td>0.30 ± 0.04</td><td>0.38 ± 0.06</td></tr><tr><td>Random Forest</td><td>Balanced</td><td>Hybrid</td><td>0.27 - 0.33</td><td>0.32 - 0.40</td></tr><tr><td>Gradient Boosting</td><td>Balanced</td><td>Hybrid</td><td>0.26 - 0.32</td><td>0.34 - 0.42</td></tr><tr><td>Linear Regression</td><td>Balanced</td><td>Hybrid</td><td>0.44 - 0.50</td><td>0.60 - 0.68</td></tr><tr><td>Ridge Regression</td><td>Balanced</td><td>Hybrid</td><td>0.35 - 0.41</td><td>0.36 - 0.44</td></tr><tr><td>Lasso Regression</td><td>Balanced</td><td>Hybrid</td><td>0.33 - 0.39</td><td>0.34 - 0.42</td></tr><tr><td>Support Vector Regressor</td><td>Balanced</td><td>Hybrid</td><td>0.25 - 0.31</td><td>0.29 - 0.37</td></tr></tbody></table>

Feature importance analysis played a critical role in guiding the selection of raw features, emphasizing variables with known clinical relevance. For example, Triglyceride and Serum Creatinine were retained due to their established roles in cardiovascular risk, aligning statistical findings with domain expertise. This approach ensured that the reduced feature set preserved interpretability, enabling clinicians to derive actionable insights from model predictions.

Balancing techniques improved model performance across most scenarios, further highlighting the importance of addressing dataset imbalances. Linear Regression, for example, saw a slight improvement in RMSE from 0.22 ± 0.03 in the unbalanced scenario to 0.23±0.03 in the balanced scenario. However, these improvements were not
uniform across all models. Gradient Boosting demonstrated robustness, with minimal performance changes between balanced and unbalanced datasets, while simpler models like k-NN struggled to handle imbalanced data effectively. These findings reinforce the need for preprocessing strategies tailored to both the dataset and the model.

Hybrid preprocessing, which combined PCA with feature selection, demonstrated the potential to enhance performance while preserving interpretability. For instance, Random Forest under the hybrid approach achieved an MAE of 0.27 to 0.33 and RMSE of 0.32 to 0.40, outperforming PCA-only preprocessing. These results underscore the benefits of integrating domain knowledge with statistical techniques to optimize performance in high-dimensional datasets.

**Challenges and Implications.** While the findings highlight promising directions for predictive modeling, they also underscore the limitations of high-dimensional, small-sample datasets. PCA's ability to reduce dimensionality must be balanced against the risk of obscuring clinically relevant features. Feature selection mitigates this risk by prioritizing interpretability, but it requires careful alignment with domain knowledge to avoid excluding important predictors.

From a clinical perspective, these results suggest that hybrid preprocessing strategies and ensemble methods like Random Forest and Gradient Boosting are well-suited for predicting artery blockage in high-dimensional datasets. Linear models remain valuable for datasets with strong linear relationships, though their simplicity may limit generalizability to more complex patterns.

**Future Directions.** To address the limitations of small sample sizes and limited feature variability, future studies should focus on augmenting datasets through multicenter collaborations. Additionally, incorporating clinically informed feature selection alongside advanced dimensionality reduction techniques could further enhance model performance. These efforts are essential for translating predictive models into actionable tools for cardiovascular risk assessment.

# 5 Limitations

This study demonstrates the potential of machine learning for predicting atherosclerosis risk using small, high-dimensional datasets, but several limitations warrant attention. (a) The dataset's small size (31 patients, 80 observations) limits statistical power, increases overfitting risk, and reduces generalizability. (b) High missingness in key features (e.g., abnormal ECG, triglycerides) further affects reliability, despite advanced imputation methods like MICE. (c) Excluding asymptomatic patients restricts findings to symptomatic individuals, limiting applicability to early or subclinical stages of atherosclerosis. (d) The single-center data introduces demographic and procedural biases, which could be mitigated by multi-center, diverse datasets. (e) Additionally, the absence of external validation on independent datasets limits confidence in the models' generalizability. While appropriate for the dataset size, the exclusion of advanced techniques like transfer learning constrains exploration of methods that could address small dataset challenges. (f) The study also does not account for real-world complexities, such as treatment effects, longitudinal outcomes, or patient
adherence. Addressing these limitations through expanded datasets, external valida-
tions, and advanced machine learning approaches will enhance the findings' robustness
and clinical relevance.

6 Future Directions

Future efforts should involve multi-center collaborations across tertiary care clinics within the state to collect larger, more diverse patient cohorts, improving generalizability and reducing biases. Key areas to address include: (a) leveraging diverse clinic data to mitigate demographic and procedural biases; (b) integrating imaging data (e.g., CT or MRI) with biochemical and clinical features for multimodal analysis; and (c) using synthetic data augmentation (GANs, VAEs) and transfer learning to enhance model robustness, particularly for underrepresented subpopulations.

External validation on datasets from multiple clinics is essential to ensure reliability and consistency across independent cohorts. Temporal modeling of longitudinal data from these centers can capture disease progression while accounting for treatment adherence and comorbidities. Including asymptomatic populations through innovative screening strategies at clinics and outreach programs can address gaps in detecting subclinical disease. Finally, integrating multimodal data sources—genetic, imaging, and biochemical markers—into unified frameworks will enhance risk stratification and personalized care, advancing early detection and improving cardiovascular health outcomes statewide.

7 Conclusions

This study demonstrates the feasibility and effectiveness of applying machine learning
techniques to address the challenges of high-dimensional, small-sized clinical datasets
in atherosclerosis risk prediction. Despite the constraints of dataset size and miss-
ing data, the use of advanced preprocessing techniques—for imputation, SMOTE
for class balancing, and PCA for dimensionality reduction—enabled the extraction of
meaningful insights. Models such as Random Forest and XGBoost achieved high pre-
dictive performance, with AUC-ROC values exceeding 0.93, underscoring the potential
of ensemble learning methods in clinical prediction tasks. However, this study should
be viewed as a proof-of-concept rather than a definitive solution. The small sample
size, lack of external validation, and single-center data highlight the need for cau-
tious interpretation and future refinement. Expanding datasets through multi-center
collaborations, incorporating imaging and genetic data, and validating models on
independent cohorts are necessary next steps to enhance generalizability and clinical
relevance. This research also underscores the importance of integrating explainable AI
techniques to improve interpretability and clinical adoption. While the methodologies
presented here are scalable, their real-world utility depends on aligning machine learn-
ing outputs with actionable clinical insights. By addressing these challenges, this work
lays the foundation for more accurate, interpretable, and scalable predictive models
for atherosclerosis. These advancements have the potential to significantly improve
early detection, risk stratification, and personalized care, ultimately contributing to
better patient outcomes in cardiovascular health.
## Abbreviations

<table><tr><td>AUC-ROC</td><td>Area Under the Receiver Operating Characteristic Curve</td></tr><tr><td>CVD</td><td>Cardiovascular Disease</td></tr><tr><td>CT</td><td>Computed Tomography</td></tr><tr><td>DBSCAN</td><td>Density-Based Spatial Clustering of Applications with Noise</td></tr><tr><td>ECG</td><td>Electrocardiogram</td></tr><tr><td>EM</td><td>Expectation-Maximization</td></tr><tr><td>GAN</td><td>Generative Adversarial Network</td></tr><tr><td>GAI</td><td>Generative Artificial Intelligence</td></tr><tr><td>GMM</td><td>Gaussian Mixture Model</td></tr><tr><td>HDL</td><td>High-Density Lipoprotein</td></tr><tr><td>KNN</td><td>K-Nearest Neighbor</td></tr><tr><td>LAD</td><td>Left Anterior Descending Artery</td></tr><tr><td>LCX</td><td>Left Circumflex Artery</td></tr><tr><td>LDL</td><td>Low-Density Lipoprotein</td></tr><tr><td>LIME</td><td>Local Interpretable Model-agnostic Explanations</td></tr><tr><td>LM</td><td>Left Marginal Artery</td></tr><tr><td>LSTM</td><td>Long Short-Term Memory</td></tr><tr><td>MICE</td><td>Multiple Imputation by Chained Equations</td></tr><tr><td>MRI</td><td>Magnetic Resonance Imaging</td></tr><tr><td>OC-SVM</td><td>One-Class Support Vector Machine</td></tr><tr><td>PCA</td><td>Principal Component Analysis</td></tr><tr><td>RCA</td><td>Right Coronary Artery</td></tr><tr><td>SHAP</td><td>SHapley Additive exPlanations</td></tr><tr><td>SMOTE</td><td>Synthetic Minority Oversampling Technique</td></tr><tr><td>SVM</td><td>Support Vector Machine</td></tr><tr><td>UMAP</td><td>Uniform Manifold Approximation and Projection</td></tr><tr><td>VAEs</td><td>Variational Autoencoders</td></tr><tr><td>XAI</td><td>Explainable Artificial Intelligence</td></tr><tr><td>XGBoost</td><td>eXtreme Gradient Boosting</td></tr></table>

## Declarations

### Ethics Approval

This study did not involve any interventions or experiments with human participants or animals requiring ethics approval. Therefore, ethics approval was not applicable.

### Consent for Participation and Publication

As this study utilized de-identified clinical data and did not involve direct interaction with human subjects, informed consent for participation and publication was not required.
**Availability of Data and Materials**

The dataset used in this study is not publicly available due to patient privacy concerns
but can be made available from the corresponding author upon reasonable request
and with appropriate ethical approvals.

**Competing Interests**

The authors declare that they have no competing interests related to this manuscript.

**Funding**

No external funding was received for this study.

**Authors' Contributions**

* **A.A.W.** conceived the study, developed the methodology, and conducted the data analysis.

* **F.A.** contributed to the interpretation of the results and provided clinical insights.

* **A.F.** participated in the literature review and manuscript writing.

* All authors contributed to the manuscript's revision and approved the final version.

**Acknowledgements**

The authors would like to thank the Cardiology Department of Government Medical
College, Srinagar, for providing the clinical data used in this study.

**Clinical Trial Registration**

This study does not involve a clinical trial, so registration is not applicable.

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