A Comparative Analysis of Machine Learning Algorithms in Predicting Cardiovascular Disease Risk: Evaluating Performance Metrics and Clinical Implications

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating accurate risk prediction models. This study aims to critically evaluate the performance of various machine learning algorithms, including Random Forest, Support Vector Machines, and Neural Networks, in predicting CVD risk. We employed a comprehensive dataset of over 10,000 patient records, utilizing stratified cross-validation to ensure robust performance metrics. Key findings revealed that the Neural Network model outperformed traditional logistic regression, achieving a sensitivity of 92%, specificity of 89%, and an area under the ROC curve (AUC) of 0.94. These results indicate that advanced machine learning techniques offer superior precision in risk stratification compared to conventional methods, suggesting significant implications for clinical decision-making. Our findings underscore the urgent need for integrating these models into clinical practice to enhance patient outcomes and resource allocation.