The Utilization of Decision Tree Algorithm In Order to Predict Heart Disease

The data on heart disease patients obtained from the Ministry of Health Republic Indonesia in 2020 explains that has increased every year and ranks as highest cause death Indonesia, especially at productive ages. If people with are not treated properly, then their effective period a patient can experience more quickly. Thus, predictive model is able to help medical personnel solve health problems built. This study employed Random Forest Decision Tree algorithm classification process by processing cardiac create based obtained, showing was balanced. overcome imbalance, an oversampling technique carried out using ADASYN SMOTE. proved performance SMOTE techniques C45 Classifier had significant effect prediction results. usage analyze aims handle unbalanced datasets, confusion matrix used for testing Precision, Recall, F1-SCORE, well Accuracy. Based results research been K-Fold 10 technique, + RF one best which greater Accuracy 93.58% compared without 90.51% 93.55%. application proven be problem imbalance get better than technique.