## 2 Literature Review
### Importance of Feature Selection and Insights  
Feature selection plays a crucial role in constructing reliable and understandable prediction models. In the healthcare domain, features like age, cholesterol levels, blood pressure, diabetes, hypertension, and smoking status are common impactful risk factors for CVDs [44; 45]. However, modern studies have highlighted that factors like body mass index (BMI), stress levels, and aberrant resting ECG are known to be significant predictors of CVD mortality [46; 47; 48].  
In order to increase ML models' forecast accuracy, feature selection is critical. [PERSON] and [PERSON] [49] emphasized the significance of statistically validating features in medical ML models, especially for HF prediction. Although biomarkers are ranked using conventional feature importance approaches, they often lack statistical significance assessment. To address this, a novel approach was put out to maximize biomarker selection and assess the relevance of feature importance values. This method improved diagnosis, therapy, and disease management by guaranteeing statistically significant traits, which increased the dependability of clinical decision-making. [PERSON] et al. [50] demonstrated that the performance of the reduced models was enhanced by the selected features when compared to full models that utilized all available data and had 62 features. RF, combined with recursive feature elimination (RFE) and RF importance (RFI), achieved a higher area under the precision-recall curve (AUPRC) scores (0.8048-0.8505). AUPRC of 0.8462 for a five-feature model (troponin I, High-Density Lipoprotein (HDL) cholesterol, HbA1c, anion gap, and albumin) indicated the importance of these characteristics in AMI prediction.  
[PERSON] et al. [51] used feature selection to improve the model's performance by refining relevant text-based features through Term Frequency--Inverse Document Frequency (TF-IDF) and handcrafted techniques. The model attained up to 100% accuracy in cross-validation and 95% on unseen data by combining feature selection with data augmentation and hyperparameter tuning, highlighting its crucial role in maximizing accuracy. In another study, [PERSON] et al. [52] reduced the feature set to 50 by using the RFE method to filter out the most pertinent predictors for MI complications. By using this RFE, XGBoost proved to be the best model, achieving a high accuracy of 91.47% and an F1-score of 95.14% in identifying fatal complications. [PERSON] et al. [53] used clinical data from 3061 cases to develop a stacking ensemble model to predict in-hospital mortality in patients with non-ST-segment elevation myocardial infarction (NSTEMI). The technique used over-sampling to address class imbalance, RFE for feature selection, and a two-layer stacking model that integrated seven base classifiers with XGBoost as the meta-model. With an AUC of 0.987, the model outperformed standalone classifiers such as RF, SVM, DT, and LR.  
[PERSON] et al. [54] used the SelectKBest function in conjunction with chi-squared statistical analysis for FE in order to improve predictive accuracy. Two datasets were used to train optimized classifiers, with the RF model achieving accuracy scores of 100% and 97.68% across datasets, as well as precision, recall, and F1 scores occasionally reaching 100%. [PERSON] et al. [55] emphasized the importance of unbiased feature selection in improving MI risk prediction. Despite dataset imbalances, the Triple Criteria Selection (TCS) approach was developed to refine the most pertinent self-reported features and ensure model reliability. The Dual-Path Artificial Neural Network (DP-ANN) and Minority Weighted Sampling (MWS) were integrated with TCS to reduce bias and produce balanced performance with Area Under the Curve--Receiver Operating Characteristic evaluation metric (AUC-ROC: 89.5%). Key predictors were further validated by SHAP analysis, emphasizing the vital role feature selection plays in improving MI risk assessment and early identification.