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http://dx.doi.org/10.9723/jksiis.2022.27.5.073

Analysis of Hypertension Risk Factors by Life Cycle Based on Machine Learning  

Kang, SeongAn (동아대학교 경영정보학과)
Kim, SoHui (동아대학교 경영정보학과)
Ryu, Min Ho (동아대학교 경영정보학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.5, 2022 , pp. 73-82 More about this Journal
Abstract
Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.
Keywords
Hypertension; Tree-based Machine Learning; Lifecycle; Feature Importance;
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