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

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 metabol...

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Vydané v:JMIR medical informatics Ročník 8; číslo 3; s. e17110
Hlavní autori: Yu, Cheng-Sheng, Lin, Yu-Jiun, Lin, Chang-Hsien, Wang, Sen-Te, Lin, Shiyng-Yu, Lin, Sanders H, Wu, Jenny L, Chang, Shy-Shin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Canada JMIR Publications 01.03.2020
Predmet:
Algorithms
Biopsy
Blood pressure
Cholesterol
Classification
Cohort analysis
Data collection
Decision trees
Fasting
Hemoglobin
Hospitals
Insulin resistance
Kidney diseases
Laboratories
Liver
Machine learning
Metabolic syndrome
Original Paper
Overweight
Triglycerides
Ultrasonic imaging
Womens health
Taiwan
metabolic syndrome
decision tree
controlled attenuation parameter technology
machine learning
ISSN:2291-9694, 2291-9694
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Shrnutí: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. 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. 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. 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. Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.
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ISSN:2291-9694
2291-9694
DOI:10.2196/17110