| Title: | Machine Learning-Driven Metabolic Syndrome Prediction: An International Cohort Validation Study |
| Journal: | Healthcare |
| Published: | 13 Dec 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39765954/ |
| DOI: | https://doi.org/10.3390/healthcare12242527 |
Publication 13990
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Abstract
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.</p>
3 Authors
- Zhao Li
- Wenzhong Wu
- Hyunsik Kang
Enabling scientific discoveries that improve human health