Factors influencing metabolic syndrome perception and exercising behaviors in Korean adults: Data mining approach

대사증후군의 인지와 신체활동 실천에 영향을 미치는 요인: 데이터 마이닝 접근

  • Lee, Soo-Kyoung (College of Nursing, The Research Institute of Nursing Science, Keimyung University) ;
  • Moon, Mikyung (College of Nursing, The Research Institute of Nursing Science, Kyungpook National University)
  • 이수경 (계명대학교 간호대학, 간호과학연구소) ;
  • 문미경 (경북대학교 간호대학, 간호과학연구소)
  • Received : 2017.11.06
  • Accepted : 2017.12.08
  • Published : 2017.12.31


This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata.


Supported by : Keimyung University


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