Machine Learning-Driven Metabolic Syndrome Prediction: An International Cohort Validation Study

被引:0
作者
Li, Zhao [1 ]
Wu, Wenzhong [1 ]
Kang, Hyunsik [1 ]
机构
[1] Sungkyunkwan Univ, Coll Sport Sci, Suwon 16419, South Korea
关键词
metabolic syndrome; diabetes; cardiovascular disease; machine learning; risk assessment; HEALTH;
D O I
10.3390/healthcare12242527
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background/Objectives: This study aimed to develop and validate a machine learning (ML)-based metabolic syndrome (MetS) risk prediction model. Methods: We examined data from 6155 participants of the China Health and Retirement Longitudinal Study (CHARLS) in 2011. The LASSO regression feature selection identified the best MetS predictors. Nine ML-based algorithms were adopted to build predictive models. The model performance was validated using cohort data from the Korea National Health and Nutrition Examination Survey (KNHANES) (n = 5297), the United Kingdom (UK) Biobank (n = 218,781), and the National Health and Nutrition Examination Survey (NHANES) (n = 2549). Results: The multilayer perceptron (MLP)-based model performed best in the CHARLS cohort (AUC = 0.8908; PRAUC = 0.8073), the logistic model in the KNHANES cohort (AUC = 0.9101, PRAUC = 0.8116), the xgboost model in the UK Biobank cohort (AUC = 0.8556, PRAUC = 0.6246), and the MLP model in the NHANES cohort (AUC = 0.9055, PRAUC = 0.8264). Conclusions: Our MLP-based model has the potential to serve as a clinical application for detecting MetS in different populations.
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页数:22
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