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

Abstract

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

Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung

“…H. Ramadhan, 2022). Sebelumnya, teknik data mining terbukti mampu membantu untuk menganalisa dan membantu paramedis untuk melakukan diagnosis dini penyakit jantung pada pasien (Annisa, 2019;Derisma, 2020;Firdlous, 2022;Handayani, 2021;Mia et al, 2022;Sonjaya et al, 2022).…”

“…Hasil akurasi menunjukkan bahwa algoritma Random forest memiliki performa yang lebih baik berdasarkan nilai akurasi sebesar 85.7%. Selanjutnya, penelitian yang dilakukan oleh Mia et al (2022) dan Sonjaya et al (2022)…”

“…I. K-Fold Cross Validation K-Fold Cross-Validation adalah teknik yang umum digunakan dalam pembelajaran mesin dan statistik untuk mengevaluasi kinerja model atau algoritma (Djatna et al, 2018;Masruriyah et al, 2019;Mia et al, 2022;Nur Masruriyah et al, 2021;Sonjaya et al, 2022). Ini digunakan untuk mengukur sejauh mana model yang dikembangkan mampu melakukan generalisasi pada data yang belum pernah dilihat sebelumnya.…”