Metabolic Syndrome Prediction Models Using Machine Learning and Sasang Constitution Type.

ABSTRACT:

Background

Machine learning may be a useful tool for predicting metabolic syndrome (MetS), and previous studies also suggest that the risk of MetS differs according to Sasang constitution type. The present study investigated the development of MetS prediction models utilizing machine learning methods and whether the incorporation of Sasang constitution type could improve the performance of those prediction models.

Methods

Participants visiting a medical center for a health check-up were recruited in 2005 and 2006. Six kinds of machine learning were utilized (K-nearest neighbor, naive Bayes, random forest, decision tree, multilayer perceptron, and support vector machine), as was conventional logistic regression. Machine learning-derived MetS prediction models with and without

SUBMITTER: Park JE 

PROVIDER: S-EPMC7886522 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature