## 2 Materials and Methods
### Data Analysis  
Data preprocessing was carried out using Python's pandas library [36]. The dataset comprised 162 instances across 13 variables, with 9 variables retained for analysis. Categorical variables were converted to numerical values; for example, sex was encoded as 1 for male and 0 for female. Age was categorized into six groups (0-19, 20-25, 26-35, 36-50, 51-65, 65+) and encoded numerically from 0 to 5. The tag for analysis was transformed into three categorical values: 0 for health, 1 for neuro, and 2 for other disease profiles. Health represents a cohort with no symptoms of dementia or cognitive deficits, neuro represent patients diagnosed with MCI and AD, and other denotes a cohort with the positive expression of TNF-\(\alpha\), IL-1\(\beta\), and IL-6 Ra; smoking; and a neurodegenerative history.  
Data visualization was performed using the matplotlib [37] and seaborn [38] libraries. A correlation heatmap using [PERSON]'s correlation coefficient was created to examine the inter-variable relationships and their associations with the dataset's tag (profile). Dimensionality reduction was conducted using principal component analysis (PCA) [39],Uniform Manifold Approximation and Projection (UMAP) [40], and t-Distributed Stochastic Neighbor Embedding (t-SNE) [41], with visual representations generated in both 2D and 3D formats.  
Feature importance was assessed using a hybrid approach [42] combining three gradient boosting classifiers: XgBoost [43], CatBoost [44], and LightGBM [45]. These algorithms ranked the features based on their importance, and the rankings were aggregated using a Borda rank-based count method. A line/dot plot was constructed to display the feature importance values.  
Multi-class classification was conducted using the random forest algorithm [46], with a 10-fold cross-validation procedure to evaluate the performance. This particular classifier was selected due to its well-established strong performance on complex biomedical datasets, combined with its interpretability; it offers a valuable feature importance metric to assess the significance of individual features. The results were synthesized into a confusion matrix. Additionally, a decision tree classifier [47] was configured with a maximum depth of 10, a minimum of 15 samples required to split an internal node, and a minimum of 5 samples per leaf node to maintain model interpretability.  
These combined biological and computational methodologies ensured a comprehensive analysis, integrating detailed cellular observations with advanced data processing and machine learning techniques to provide robust insights into the dataset. This study aims to bridge the gap between clinical and computational methodologies to enhance the understanding and detection of Alzheimer's disease. By integrating detailed cellular observations with advanced data processing and machine learning techniques, the research seeks to identify significant biomarkers and patterns associated with AD. The goal is to develop minimally invasive, cost-effective, and reliable methods for the early diagnosis and prevention of AD, thereby contributing to better management and potentially mitigating the impact of this neurodegenerative disorder on the aging population.