A Comparative Analysis of Machine Learning Algorithms for the Early Prediction of Heart Disease

Abstract: Heart disease remains one of the leading causes of global mortality, creating a growing need for accurate and reliable early diagnostic systems. The purpose of this study is to compare selected machine learning algorithms for the early prediction of heart disease and evaluate their suitability for clinical decision-making. The study specifically examines the performance of Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) using a clinical dataset of 1000 patient records. The objectives include evaluating model performance through accuracy, precision, recall, and F1-score metrics while identifying significant cardiovascular risk predictors.

The study adopts a quantitative and comparative research design supported by descriptive statistical analysis and machine learning techniques. The findings reveal that Random Forest achieved the highest predictive performance, while Logistic Regression provided better interpretability and transparency for clinical applications. Variables such as chest pain type, exercise angina, ST segment slope, and thallium test results were identified as significant predictors of heart disease. The study concludes that machine learning models can effectively support early heart disease prediction and improve clinical decision-making, provided that predictive accuracy is balanced with interpretability and transparency.

Keywords: Heart Disease Prediction, Machine Learning, Random Forest, Logistic Regression, Cardiovascular Risk Predictive Analytics