Implementasi Algoritma Decision Tree untuk Klasifikasi Serangan Jantung - Muhammad Akram Fais; M. Revano Ananda Lubis; Annisa Aulia; Indri Syafitri (Jurnal Sistem Informasi dan Ilmu Komputer, 2023)
As many as 7.3 million people worldwide die from heart disease. This indicates that heart disease is one of the diseases that cause the most deaths. As a preventive effort in handling heart disease, it is necessary to predict heart disease in patients. The classification process to predict heart disease is done using a decision tree. This decision tree is interesting because it is more flexible in providing the advantage of visualizing the advice so that the prediction can be observed. This study uses Heart Disease Prediction Dataset data with a total of 303 data. Then predictions are made using Decision tree so that the accuracy results are 83.60%, precision 89.28%, recall 78.12% and F1 score of 83.33%.