Predicting the risk of metabolic-associated fatty liver disease in the elderly population in China: construction and evaluation of interpretable machine learning models
Machine learning models, particularly the random forest algorithm, can effectively predict the risk of MAFLD in the elderly population. These models may assist clinicians in early screening and intervention, thereby improving patient outcomes.
Authors
- Yingxin Zeng
- Guodong Xia (ORCID: https://orcid.org/0000-0002-7711-8547)
- Xinmei Zhang (ORCID: https://orcid.org/0000-0001-7122-6435)
- Chao Yang
- Xin Yang
Institutions
- Affiliated Hospital of Southwest Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-10-20
- DOI
- https://doi.org/10.3389/fmed.2025.1678076
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Southwest Medical University
- Affiliated Hospital of Southwest Medical University