Classification of Heart Disease Risk Factors Using Decision Tree at Rantauprapat Regional Hospital

Heart disease is one of the leading causes of death in Indonesia, so it is important to identify risk factors that contribute to the increasing incidence of heart disease. This study aims to classify risk factors for heart disease using the Decision Tree method with the CART (Classification and Regression Tree) algorithm at Rantauprapat Regional Hospital. The data used includes factors such as Age, High Blood Pressure, High Cholesterol Levels, Body Mass Index (BMI), Family History, Smoking, Unhealthy Diet, and Low Physical Activity. The results of the analysis show that the factors Age, High Blood Pressure, and High Cholesterol Levels have a significant effect on the increased risk of heart disease, with a model accuracy of 80%. Although this model successfully classifies high risk well, there are some errors in identifying low risk, as reflected in the Recall value (0.67).

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Journal
International Journal of Health Engineering and Technology
Published
2025-05-20
DOI
https://doi.org/10.55227/ijhet.v3i4.273
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Classification of Heart Disease Risk Factors Using Decision Tree at Rantauprapat Regional Hospital

Baginda Restu Al Ghazali, Nailatun Nadrah, R. Faisal, Quratih Adawiyah et al.
International Journal of Health Engineering and Technology
Data Mining and Machine Learning Applications
article

Classification of Heart Disease Risk Factors Using Decision Tree at Rantauprapat Regional Hospital

Baginda Restu Al Ghazali, Nailatun Nadrah, R. Faisal, Quratih Adawiyah, Juni Purwanto
article en

Abstract

Heart disease is one of the leading causes of death in Indonesia, so it is important to identify risk factors that contribute to the increasing incidence of heart disease. This study aims to classify risk factors for heart disease using the Decision Tree method with the CART (Classification and Regression Tree) algorithm at Rantauprapat Regional Hospital. The data used includes factors such as Age, High Blood Pressure, High Cholesterol Levels, Body Mass Index (BMI), Family History, Smoking, Unhealthy Diet, and Low Physical Activity. The results of the analysis show that the factors Age, High Blood Pressure, and High Cholesterol Levels have a significant effect on the increased risk of heart disease, with a model accuracy of 80%. Although this model successfully classifies high risk well, there are some errors in identifying low risk, as reflected in the Recall value (0.67).

International Journal of Health Engineering and TechnologyVol. 3(4)
Information Technology Institute (EG), Euskadiko Parke Teknologikoa (ES), Research Institute of Health Sciences (ES), Health & Life (Taiwan) (TW)
Good health and well-being
Openalex Percentile: Top 69%
Data Mining and Machine Learning Applications
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Classification of Heart Disease Risk Factors Using Decision Tree at Rantauprapat Regional Hospital — Baginda Restu Al Ghazali, Nailatun Nadrah, et al. · International Journal of Health Engineering and Technology (2025) | TGRS Research Map | TGRS