Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive exPlanations
This study demonstrates the effectiveness of integrating body composition features with machine learning techniques for MAFLD risk prediction. The GBM model offers robust predictive accuracy and interpretability, with potential applications in clinical decision-making and public health screening strategies. SHAP analysis provides meaningful insights into the relative importance of adiposity measures, reinforcing the value of fat distribution metrics beyond conventional obesity indices.
Authors
- Weining Xie (ORCID: https://orcid.org/0000-0001-8377-4391)
- Ling Wang (ORCID: https://orcid.org/0000-0002-7621-2312)
- Fan Zhang (ORCID: https://orcid.org/0000-0002-3658-9887)
- Hong Yan (ORCID: https://orcid.org/0000-0002-7475-0109)
- Xinrong Chen (ORCID: https://orcid.org/0000-0003-2561-8694)
- Zhirong Zeng
Institutions
- Guangzhou University of Chinese Medicine (CN)
- Integrated Chinese Medicine (China) (HK)
- Foshan Hospital of TCM (CN)
- Guangdong Provincial Hospital of Traditional Chinese Medicine (CN)
- The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine (CN)
- Guangxi University of Chinese Medicine (CN)
Publication Details
- Journal
- Frontiers in Nutrition
- Published
- 2025-06-30
- DOI
- https://doi.org/10.3389/fnut.2025.1616229
- Citations
- 5
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.77
Funders
- Basic and Applied Basic Research Foundation of Guangdong Province