Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population. The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People&#...

Full description

Saved in:
Bibliographic Details
Published in:Diabetes, metabolic syndrome and obesity metabolic syndrome and obesity, 2023-07, Vol.16, p.2141-2151
Main Authors: Xu, Wei, Zhang, Zikai, Hu, Kerong, Fang, Ping, Li, Ran, Kong, Dehong, Xuan, Miao, Yue, Yang, She, Dunmin, Xue, Ying
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Body mass index
Computational linguistics
Economic aspects
Language processing
Machine learning
machine learning methods
metabolic syndrome
naive bayesian
Natural language interfaces
Neural networks
non-invasive method
Original Research
Type 2 diabetes
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population. The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%. This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.
ISSN:1178-7007
1178-7007
DOI:10.2147/DMSO.S413829