From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in adolescents
The ET-based ML model provides an accurate and interpretable approach for adolescent NAFLD risk prediction. By surpassing traditional metabolic indicators and offering an accessible web-based calculator, it supports scalable, cost-effective early screening and targeted prevention strategies.
Authors
- Rong Wang (ORCID: https://orcid.org/0000-0001-9240-6726)
- Chenming Zhang (ORCID: https://orcid.org/0000-0003-1426-0092)
- Liaoyun Zhang (ORCID: https://orcid.org/0000-0002-3682-0516)
- Bin Niu
Institutions
- Academy of Medical Sciences (GB)
- Shanxi Medical University (CN)
- First Hospital of Shanxi Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-10-29
- DOI
- https://doi.org/10.3389/fendo.2025.1681686
- Citations
- 1
- Primary Topic
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Type
- article
- Field-Weighted Citation Impact
- 1.29
Funders
- ShanXi Science and Technology Department