Machine learning-driven predictions of metabolic syndrome in adults: evidence from a Kurdish cohort in Iran
This study showed that in addition to aggressive models, non-invasive models (anthropometric indices, blood pressure and energy intake) can be a good and convenient screening tool for predicting MetS. The models can be used in clinical diagnosis as well as in research on large populations.
Authors
- Neda Izadi (ORCID: https://orcid.org/0000-0002-6373-1113)
- Mitra Darbandi (ORCID: https://orcid.org/0000-0002-9173-8814)
- Farid Najafi (ORCID: https://orcid.org/0000-0001-8530-7180)
- Narmin Mirzaei
- Shayan Mostafaei (ORCID: https://orcid.org/0000-0002-1966-1306)
- Yahya Pasdar (ORCID: https://orcid.org/0000-0001-8682-5721)
Institutions
- Kermanshah University of Medical Sciences (IR)
- Research Institute for Endocrine Sciences (IR)
- Shahid Beheshti University of Medical Sciences (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1186/s40001-025-03139-5
- Primary Topic
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Health and Medical Education
- Kermanshah University of Medical Sciences