Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models

Abstract

The accurate and early detection of coronary heart disease (CHD) is crucial for reducing mortality rates. This study evaluates the predictive performance of three machine learning models—Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—for classifying CHD. The models were trained on a comprehensive heart disease dataset and assessed using metrics such as Accuracy, Specificity, Sensitivity, F1-score, Negative Predictive Value, and Positive Predictive Value. Among the models, RF demonstrated the highest accuracy (93.5%), while SVM excelled in sensitivity (97.5%). The findings highlight the potential of machine learning techniques in clinical decision-making and personalized medicine.