Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Convergence study to detect metabolic syndrome risk factors by gender difference

성별에 따른 대사증후군의 위험요인 탐색을 위한 융복합 연구

  • 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)
  • 이소은 (고려대학교 대학원 보건과학과) ;
  • 이현실 (고려대학교 보건과학대학 보건정책관리학부)
  • Received : 2021.09.24
  • Accepted : 2021.12.20
  • Published : 2021.12.28
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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.

본 연구의 목적은 국민건강영양조사 2016-2019년 자료 중 성인을 대상으로 대사증후군의 위험요인 탐색하고, 성별에 따른 위험요인의 차이를 규명하여 대사증후군 예방 및 치료에 기초자료로 제공하기 위함이다. 다양한 선행연구를 통해 대사증후군 위험요인을 수집하고, 4개의 머신러닝(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest)의 방법을 이용하여 분석하였다. 남성과 여성 모두에서 Random Forest의 대사증후군 예측 정확도가 높았다. 대사증후군 유병에 영향을 주는 상위 위험요인으로는 여성과 남성 모두에서 BMI, 식이(지방, 비타민 C, 비타민 A, 단백질, 에너지 섭취), 기저질환의 개수, 연령으로 나타났다. 여성의 경우 교육수준과 초경 연령, 폐경 여부가 추가적으로 주요 위험요인으로 나타났고, 남성에 비해 연령과 기저질환의 개수에서 영향력이 큰 것으로 나타났다. 대사증후군을 예방하기 위해선 BMI, 식이, 질환의 이환, 초경 및 폐경여부를 고려하여 접근해야하며 후속 연구를 통해 다양한 중재 전략을 수립하고 검증해야 할 것이다.

Keywords

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