Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Cheng-Sheng Yu; Yu-Jiun Lin; Chang-Hsien Lin; Sen-Te Wang; Shiyng-Yu Lin; Sanders H Lin; Jenny L Wu; Shy-Shin Chang

doi:10.2196/17110

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

JMIR Med Inform. 2020 Mar 23;8(3):e17110. doi: 10.2196/17110.

Authors

Cheng-Sheng Yu^#^{1

2}, Yu-Jiun Lin^#^{1

2}, Chang-Hsien Lin^{1

2}, Sen-Te Wang^{1

2}, Shiyng-Yu Lin^{1

2}, Sanders H Lin¹, Jenny L Wu^{1

2}, Shy-Shin Chang^{1

2}

Affiliations

¹ Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
² Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

^# Contributed equally.

PMID: 32202504
PMCID: PMC7136841
DOI: 10.2196/17110

Abstract

Background: Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling.

Objective: We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan.

Methods: Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables.

Results: Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively.

Conclusions: Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

Keywords: controlled attenuation parameter technology; decision tree; machine learning; metabolic syndrome.

©Cheng-Sheng Yu, Yu-Jiun Lin, Chang-Hsien Lin, Sen-Te Wang, Shiyng-Yu Lin, Sanders H Lin, Jenny L Wu, Shy-Shin Chang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.03.2020.