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Development of Prediction Model for Prevalence of Metabolic Syndrome Using Data Mining: Korea National Health and Nutrition Examination Study

국민건강영양조사를 활용한 대사증후군 유병 예측모형 개발을 위한 융복합 연구: 데이터마이닝을 활용하여

  • Kim, Han-Kyoul (Department of Public Health Science, Graduate School BK21 Plus Program in Public Health Science, Korea University) ;
  • Choi, Keun-Ho (Korea Worker's Compensation & Welfare Service, Labor Welfare Research Institute, Research Department) ;
  • Lim, Sung-Won (School of Health Policy and Management, College of Health Science, Korea University) ;
  • Rhee, Hyun-Sill (Department of Public Health Science, Graduate School BK21 Plus Program in Public Health Science, Korea University)
  • 김한결 (고려대학교 대학원 보건과학과 BK21 플러스 인간생명-상호작용 융복합 사업단) ;
  • 최근호 (근로복지공단 근로복지연구원 조사연구부 통계분석2팀) ;
  • 임성원 (고려대학교 일반대학원 보건과학과) ;
  • 이현실 (고려대학교 대학원 보건과학과 BK21 플러스 인간생명-상호작용 융복합 사업단)
  • Received : 2016.01.01
  • Accepted : 2016.02.20
  • Published : 2016.02.28

Abstract

The purpose of this study is to investigate the attributes influencing the prevalence of metabolic syndrome and develop the prediction model for metabolic syndrome over 40-aged people from Korea Health and Nutrition Examination Study 2012. The researcher chose the attributes for prediction model through literature review. Also, we used the decision tree, logistic regression, artificial neural network of data mining algorithm through Weka 3.6. As results, social economic status factors of input attributes were ranked higher than health-related factors. Additionally, prediction model using decision tree algorithm showed finally the highest accuracy. This study suggests that, first of all, prevention and management of metabolic syndrome will be approached by aspect of social economic status and health-related factors. Also, decision tree algorithms known from other research are useful in the field of public health due to their usefulness of interpretation.

Keywords

Metabolic syndrome;Feature selection;Data mining;Decision tree;Logistic regression;Artificial neural network;KHNES

Acknowledgement

Grant : BK21플러스

Supported by : 고려대학교

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