Analyzing and Classifying Coronary Artery Disease Severity Using Statistical Methods and Machine Learning Techniques

Abstract

Background:Metabolic Syndrome (MS) is a cluster of risk factors, including large waist size (LWS), high blood pressure (HBP), high cholesterol levels (HDL), high blood glucose (HBG), glycemic index (GI), and hypertriglyceridemia (HTG), which collectively increase the risk of developing cardiovascular diseases such as Coronary Artery Disease (CAD). Understanding the relationship between MS and CAD severity is crucial for developing targeted prevention and treatment strategies.Methods:This study conducted an etiological and descriptive analysis to characterize the profiles of CAD patients with MS using various statistical methods. These methods included correlation analysis and odds ratio calculations to evaluate the significance of MS components. Multiple machine learning (ML) models, including Multilayer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), AdaBoost (ABT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and an ensemble Voting Classifier (VC), eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were employed to classify CAD severity and identify modifiable risk factors specific to various MS combinations. The effectiveness of these models was evaluated and compared.Results: The analysis identified HDL, HBG, and LWS as significant aggravating factors for CAD, while HTG appeared to be protective. The XGBoost model demonstrated superior predictive accuracy, achieving an accuracy of 83.12\% in predicting CAD severity, compared to other ML models. The inclusion of MS features significantly enhanced the performance of all ML models. Conclusions: The findings underscore the importance of incorporating comprehensive clinical features in predictive models for CAD. The study suggests that targeted prevention strategies and personalized treatment plans should consider the specific MS components influencing CAD severity. Future research should focus on validating these findings in larger, diverse populations and further integrating additional clinical and genetic data to refine predictive models.