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KMID : 1039620220120030173
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Korean Journal of Family Practice
2022 Volume.12 No. 3 p.173 ~ p.178
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A Machine-Learning-Based Risk Factor Analysis for Hypertension: Korea National Health and Nutrition Examination Survey 2016?2019
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Oh Tae-Seob
Kim Dong-Kyun
Won Chang-Won
Kim Sun-Young
Jeong Eun-Jin
Yang Ji-Soo
Yu Jung-Hwa
Kim Byung-Sung
Lee Joo-Hyun
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Abstract
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Background: The purpose of this study was to use machine learning to identify risk factors (other than systolic and diastolic blood pressure) forhypertension.
Methods: The study population comprised 23,170 adults (selected from the KNHANES 2016?2019), of whom 7,500 (32.4%) had hypertension. Wedeveloped machine learning-based classification models for diagnosing hypertension using the computerized demographic and examinationsurvey database of subjects from the KNHANES study. Random forest (RF)- and gradient boosting machine (GBM)-based classification algorithmswere trained with 5-fold cross-validation, and factors related to hypertension were identified through post-hoc analysis using the permutationfeature importance (PFI) technique. The classifiers used 59 variables whose data could be easily extracted on medical examination, excludingdirectly related variables like systolic and diastolic blood pressure.
Results: The classification performance of GBM (area under the curve [AUC], 0.852; 95% confidence interval [CI], 0.842?0.862) was slightly higher thanthat of RF (AUC, 0.847; 95% CI, 0.837?0.857). Post-hoc analysis of model classification using the PFI technique revealed age, cholesterol level,fraternal hypertension, education level, and height as risk factors for hypertension.
Conclusion: Although hypertension diagnosis is based on systolic and diastolic blood pressure measurements, hypertension could also be diagnosedby analyzing easily extractable variables such as age, cholesterol level, and family history of hypertension using machine learning.
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KEYWORD
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Hypertension, Machine Learning, Risk Factors, Data Mining, Artificial Intelligence
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