Optimizing Heart Disease Prediction Using SMOTE, Decision Tree, and Random Forest: A Regional Analysis Approach - Ryan Harrys Pratama; Ade Surya Budiman; Amin Nur Rais (Ali Institute of Research and Publication, 2026)
Heart disease remains the leading cause of mortality globally, including Indonesia. However, developing accurate predictive models is often hindered by class imbalance in medical datasets, where positive cases significantly outnumber negative cases. This study optimizes heart disease prediction by applying SMOTE (Synthetic Minority Oversampling Technique) regionally to Decision Tree and Random Forest algorithms using the