An interpretable machine learning model for predicting metabolic dysfunction‐associated steatotic liver disease in patients with type 2 diabetes
The model developed enables reliable and interpretable MASLD risk prediction in T2DM patients based on selected commonly available clinical data, providing a practical tool for routine identification and stratified management.
Authors
- Nan Gao
- Ji Cheng (ORCID: https://orcid.org/0000-0002-9442-3690)
- Xiaomin Ma (ORCID: https://orcid.org/0000-0003-3203-4507)
- Jiaojiao Liu
- Zhijuan Ge (ORCID: https://orcid.org/0009-0009-4585-0985)
- Zhuolin Zhou (ORCID: https://orcid.org/0009-0000-8840-0890)
Institutions
- China Pharmaceutical University (CN)
- Dalian Medical University (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- Nanjing Drum Tower Hospital (CN)
Publication Details
- Journal
- Diabetes Obesity and Metabolism
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1111/dom.70168
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00