ArticleScientific reports2022
Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- Quantifying Metabolic Syndrome Severity: Methodological Evolution, Clinical Validation, and Translational Perspectives.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Machine learning-based prediction of Metabolic Syndrome risk in the Quebec population.BMJ digital health & AI · 2026Article
- Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach.International journal of molecular sciences · 2025Article
- Association between the metabolic score for insulin resistance trajectory and new-onset metabolic syndrome: a retrospective cohort study based on health check-up data in China.Lipids in health and disease · 2025Article
- Effect of visceral fat on onset of metabolic syndrome.Scientific reports · 2025Article
- A deep hybrid learning framework with attention-enhanced feature extraction for BMI prediction based on physical fitness.Frontiers in public health · 2025Article
- Prediction of metabolic syndrome following a first pregnancy.American journal of obstetrics and gynecology · 2024Article
- A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.Scientific reports · 2024Article
- Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Predictive analysis of metabolic syndrome based on 5-years continuous physical examination data.Scientific reports · 2023Article
- Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study.Heliyon · 2022Article
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.R. China. Specifically, the prediction for MetS takes the numerical features of examination records as well as the differential features by using the examination records over the past two consecutive years, namely, the differential numerical feature (DNF) and the differential state feature (DSF), and the risk factors of the above features w.r.t different ages and genders are statistically analyzed. From numerical results, it is shown that the proposed DSF in addition to the numerical feature of examination records, significantly contributes to the risk prediction of MetS. Additionally, the proposed scheme, by using the proposed features, yields a superior performance to the state-of-the-art MetS prediction model, which provides the potential of effective prescreening the occurrence of MetS.
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