Machine Learning Classification for Detecting Heart Disease with K-NN Algorithm, Decision Tree and Random Forest

Ambran Hartono; Lizky Azka Dewi; Elvan  Yuniarti; Salsabila Tahta Hirani Putri; Try Surya  Harahap

doi:10.24036/eksakta/vol23-iss04/461

EKSAKTA: Berkala Ilmiah Bidang MIPA

Open Access

Machine Learning Classification for Detecting Heart Disease with K-NN Algorithm, Decision Tree and Random Forest

Ambran Hartono ¹

, Lizky Azka Dewi ¹

, Elvan Yuniarti ¹

, Salsabila Tahta Hirani Putri ²

, Try Surya Harahap ³

¹ Department of Physics, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta, Tangerang Selatan, Indonesia

² Department of Informatics System, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta, Tangerang Selatan, Indonesia.

³ Southeast Asia Biodiversity Research Institute, Chinese Academy of Sciences & Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303, China

Published 2023-12-30

Vol. 24 No. 04 (2023) PAGES 513-522

DOI 10.24036/eksakta/vol23-iss04/461

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Abstract

The heart is one of the most important organs for human; therefore, it needs to always be looked after and maintained properly. If it is not looked after and maintained properly, it will be at risk of disease. Currently, heart disease of various types still ranks first in deaths both in Indonesia and abroad. Various efforts continue to be developed by relevant scientists to detect it. Considering the importance of development efforts, in this research a machine-learning program was designing to classify heart disease as a detection system effort. In this article we will describe the analysis of the characteristics of the K-NN classifier, decision tree, random forest (accuracy, precision and recall), as well as determining the best classifier for detecting heart disease. To support the analysis of test results, Python and Google Colab programming has been implementation here. The best results obtained from the analysis of the application of these three models are the Decision Tree Classifier with accuracy, precision and recall values â€‹â€‹of 90%, 87% and 88% respectively. These results indicate that this model has been successfully developing.

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