Heart disease prediction using machine learning models - Vernando, Deden; Manurung, Jonson; Saragih, Hondor (Journal of Intelligent Decision Support System (IDSS), 2025)
Heart disease remains one of the leading causes of death globally, with mortality rates continuing to rise each year. Early detection is critical to reducing the burden of this disease; however, conventional diagnostic methods are often costly, time-consuming, and reliant on specialist expertise. This study aims to evaluate the effectiveness of four machine learning (ML) algorithms—Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—in predicting heart disease using clinical datasets. The methodology involves data preprocessing, feature selection using the Random Forest algorithm, and performance evaluation through metrics such as accuracy, precision, recall, F1-score, and support. Experimental results indicate that KNN achieved the highest accuracy after feature selection, while SVM demonstrated the highest recall despite lower precision. RF offered the most balanced performance, making it a reliable model for real-world medical applications. These findings highlight the importance of selecting appropriate algorithms and features to improve the performance of predictive models. The study suggests that future research should incorporate larger datasets, apply systematic hyperparameter tuning, and explore deep learning techniques to further enhance prediction accuracy.