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http://dx.doi.org/10.14400/JDC.2021.19.12.477

Convergence study to detect metabolic syndrome risk factors by gender difference  

Lee, So-Eun (Department of Health Sciences, Graduate School, Korea University)
Rhee, Hyun-Sill (School of Health Policy and Management, College of Health Science, Korea University)
Publication Information
Journal of Digital Convergence / v.19, no.12, 2021 , pp. 477-486 More about this Journal
Abstract
This study was conducted to detect metabolic syndrome risk factors and gender difference in adults. 18,616 cases of adults are collected by Korea Health and Nutrition Examination Study from 2016 to 2019. Using 4 types of machine Learning(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest) to predict Metabolic Syndrome. The results showed that the Random Forest was superior to other methods in men and women. In both of participants, BMI, diet(fat, vitamin C, vitamin A, protein, energy intake), number of underlying chronic disease and age were the upper importance. In women, education level, menarche age, menopause was additional upper importance and age, number of underlying chronic disease were more powerful importance than men. Future study have to verify various strategy to prevent metabolic syndrome.
Keywords
Metabolic Syndrome; Risk factors; Machine learning; Prediction model; Random Forest;
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