{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T01:08:01Z","timestamp":1793840881615,"version":"4.6.1"},"reference-count":50,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Healthcare"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/healthcare12242527","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T06:53:39Z","timestamp":1734072819000},"page":"2527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine Learning-Driven Metabolic Syndrome Prediction: An International Cohort Validation Study"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhao","family":"Li","sequence":"first","affiliation":[{"name":"College of Sport Science, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4292-1315","authenticated-orcid":false,"given":"Wenzhong","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Sport Science, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8611-1873","authenticated-orcid":false,"given":"Hyunsik","family":"Kang","sequence":"additional","affiliation":[{"name":"College of Sport Science, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1177\/2047487319886404","article-title":"The metabolic syndrome\u2014What is it and how should it be managed?","volume":"26","author":"Nilsson","year":"2019","journal-title":"Eur. 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