Comparative Analysis of Machine Learning Algorithms for Predicting Heart Attack - Tsaqif, Habib Ahmad; Kirana, Dimas Indra; Fariski, Eka Efa (IJATIS: Indonesian Journal of Applied Technology and Innovation Science, 2026)
Early detection of heart attack risk is crucial for reducing mortality rates associated with cardiovascular diseases. This study aims to perform a comparative performance analysis of four machine learning algorithms: Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) in classifying heart attack risk using a clinical dataset from Kaggle. The research methodology includes data preprocessing, data splitting using a 70:30 hold-out scheme, and model evaluation through a confusion matrix and standard classification metrics. The test results indicate that Random Forest provides the superior performance with the highest accuracy of 84%. Meanwhile, the SVM and XGBoost algorithms achieved 80% accuracy each, while the Decision Tree achieved the lowest at 70%. These findings confirm that ensemble-based models like Random Forests exhibit greater stability in handling complex clinical data patterns, making them highly promising for integration into early heart health warning systems.