International Journal of Scientific Research and Engineering Development

Heart disease remains one of the leading causes of mortality worldwide. Early and accurate prediction of heart disease can significantly improve medical decision-making and patient care. This study presents a comparative analysis of machine learning algorithms for heart disease prediction. Specifically, the performance of Support Vector Machine (SVM), Decision Tree (DT), and Bagging Classifier was evaluated on benchmark clinical datasets. Results demonstrate that ensemble learning using Bagging Classifier achieves superior predictive accuracy compared to individual algorithms, highlighting its potential as a reliable tool in medical decision support systems.