ArticleJournal of advanced research2026
Machine learning ensemble meets clinical practice: developing a real-world risk prediction model for metabolic syndrome using super learner and scorecard approaches.
Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction model for postpartum stress urinary incontinence risk: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- A Machine Learning-Derived Risk Scorecard for Pneumonia Hospitalization in Japanese Old-Old Adults.Geriatrics & gerontology international · 2026Article
- A comparative analysis in a clinical cohort: multiple imputation by chained equations and a novel super learner-based imputation approach.BMC medical research methodology · 2026Article
- [Pulmonary branch embolism after shortZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Machine learning-based prediction of Metabolic Syndrome risk in the Quebec population.BMJ digital health & AI · 2026Article
- Quantifying Metabolic Syndrome Severity: Methodological Evolution, Clinical Validation, and Translational Perspectives.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
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7 authors.
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Abstract
introductionMetabolic syndrome (MetS) refers to a cluster of metabolic abnormalities that significantly increase the risk of developing cardiovascular diseases (CVDs). Traditional binary definitions of MetS fall short in capturing its severity spectrum, limiting personalized risk stratification.
objectivesWe aimed to develop a super learner model and multilevel risk scorecard to improve MetS risk prediction and support early cardiovascular risk identification.
methodsThis study included a total of 460,256 health examination records from Zhejiang, China (2018-2023), with 344,925 used for model development and 115,331 used for external validation. A super learner model combining multiple machine learning algorithms was constructed to predict MetS risk. Key predictors identified through feature selection were used to develop a logistic regression-based MetS risk scorecard, stratifying individuals into five risk levels for practical and interpretable applications.
resultsThe super learner model achieved area under the receiver operating characteristics (AUCs) of 0.816 (95 % confidence interval [CI]: 0.814-0.817) and 0.810 (95 % CI: 0.808-0.813) in the development and external validation cohorts, respectively. The risk scorecard, which incorporates ten predictors, demonstrated comparable performance, with AUCs of 0.793 (95 % CI: 0.791-0.794) and 0.788 (95 % CI: 0.785-0.791) in the respective cohorts. Based on the risk scorecard, individuals were stratified into five categories: very low, low, normal, high, and very high risk levels.
conclusionThe super learner model provides a highly accurate tool for MetS risk prediction, whereas the risk scorecard offers a practical and interpretable solution for clinical and personal use. These models enable precise risk assessment to guide prevention and improve outcomes.
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