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Development of Prediction Model for Prevalence of Metabolic Syndrome Using Data Mining: Korea National Health and Nutrition Examination Study
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  • Journal title : Journal of Digital Convergence
  • Volume 14, Issue 2,  2016, pp.325-332
  • Publisher : The Society of Digital Policy and Management
  • DOI : 10.14400/JDC.2016.14.2.325
 Title & Authors
Development of Prediction Model for Prevalence of Metabolic Syndrome Using Data Mining: Korea National Health and Nutrition Examination Study
Kim, Han-Kyoul; Choi, Keun-Ho; Lim, Sung-Won; Rhee, Hyun-Sill;
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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.
Metabolic syndrome;Feature selection;Data mining;Decision tree;Logistic regression;Artificial neural network;KHNES;
 Cited by
간 효소(AST, ALT)와 전체원인사망 위험의 관련성: 한국인유전체역학조사 자료 활용,이태용;류효선;박창수;

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