Key result
Machine learning model accurately predicts metabolic syndrome risk across international cohorts with up to ~0.91 AUC.
Why the study?
This study aimed to develop and validate a machine learning-based metabolic syndrome risk prediction model across diverse populations.
Can machine learning-based models accurately predict metabolic syndrome risk across different international populations?
Cohort (n=232,782)
Yes
Can machine learning-based models accurately predict metabolic syndrome risk across different international populations?
Machine learning models, particularly multilayer perceptron, demonstrate high accuracy in predicting metabolic syndrome across diverse international populations.
Supports ML metabolic syndrome prediction across populations; leaves open prospective outcome trials before clinical adoption.
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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Zhao et al. (2024) conducted a cohort in Metabolic syndrome (n=232,782). Machine learning-based risk prediction models (including multilayer perceptron) was evaluated on Model performance for predicting metabolic syndrome (AUC and PRAUC). A multilayer perceptron-based machine learning model accurately predicted metabolic syndrome risk across multiple international cohorts, achieving AUCs ranging from 0.8556 to 0.9101.
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