ArticlePloS one2023
Machine learning-based predictive model for prevention of metabolic syndrome.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 26 citations in OpenAlex.
- A body roundness index (BRI)-based predictive model for metabolic syndrome in perimenopausal and postmenopausal women-from a cross-sectional machine learning study to a longitudinal dynamic assessment.Annals of medicine · 2026Article
- Screening Signals of Reference-Defined Metabolic Syndrome Using HbA1c and LDL Cholesterol: An Explainable Machine Learning Study.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning ensemble meets clinical practice: developing a real-world risk prediction model for metabolic syndrome using super learner and scorecard approaches.Journal of advanced research · 2026Article
- Quantifying Metabolic Syndrome Severity: Methodological Evolution, Clinical Validation, and Translational Perspectives.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Predicting metabolic syndrome in adolescents from device-measured 24-h movement behaviors: a machine learning approach.Frontiers in public health · 2026Article
- Integrated Patient Digital and Biomimetic Twins for Precision Medicine: A Perspective.Seminars in liver disease · 2025Review
- Uncovering age-specific subtypes of pediatric obesity and metabolic syndrome using machine learning algorithms.Scientific reports · 2025Article
- Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach.International journal of molecular sciences · 2025Article
- Predictive Modeling of Weight Loss and Metabolic Health Outcomes: A Retrospective Predictive Modeling Study.Health science reports · 2025Article
- Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.International journal of obesity (2005) · 2025Article
- Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study.Journal of medical Internet research · 2025Article
- Vision transformer based interpretable metabolic syndrome classification using retinal Images.NPJ digital medicine · 2025Article
- Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study.BMC medical genomics · 2024Article
- A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.Scientific reports · 2024Article
- Article
- Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.Frontiers in endocrinology · 2024Article
- Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach.PloS one · 2024Article
- Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight.PloS one · 2024Article
- Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model's construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.