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Prediction model of hypercholesterolemia using body fat mass based on machine learning

머신러닝 기반 체지방 측정정보를 이용한 고콜레스테롤혈증 예측모델

  • Lee, Bum Ju (Dept. of Future Medicine Division, Korea Institute of Oriental Medicine)
  • Received : 2019.09.15
  • Accepted : 2019.10.20
  • Published : 2019.11.30

Abstract

The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

Acknowledgement

Supported by : 한국연구재단

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