Perbandingan Metode Decision Tree, Naïve Bayes, dan K-Nearest Neighbor (KNN) Untuk Meningkatkan Akurasi Algoritma Machine Learning Dalam Memprediksi Heart Disease (Penyakit Jantung)

Anisa Yulandari, Sri Khaerawati Nur, Ayu Hernita

Abstract

Machine Learning is a machine learning technology that can be used to facilitate work in various fields, one of which is the health sector. Machine Learning in the health sector can be used to predict or diagnose a disease that is generated based on a dataset. Heart disease is a deadly disease, especially among women, therefore there is a need for early diagnosis of heart disease so that treatment can be carried out appropriately and prevent the spread of cancer in the body. Previous research has discussed heart disease diagnosis, but the level of accuracy is still low, so there is a need for techniques to increase accuracy to be able to provide accurate information. The aim of this research is to compare the Ensemble method using Machine Learning algorithms, namely Decision Tree, Naïve Bayes, and K-Nearest Neighbor (KNN), to increase accuracy in predicting Heart disease. The Ensemble methods used in this research are Adaboost and Bagging. The research results show that there is an improvement in the classification algorithm using the Ensemble method. The most superior methods are the Decision Tree Algorithm and the Ensemble method which produces an accuracy of 82.76%. The highest AUC value was obtained from the KNN algorithm combined with the Bagging method, namely 0.950 in the very good Catagory.

Keywords

Heart disease, Machine Learning, metode Ensemble, Adaboost, Bagging

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DOI: https://doi.org/10.5281/zenodo.14377870

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