Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics
This study demonstrated that machine learning models constructed using a combination of essential demographic and clinical characteristics can accurately screen for MASLD in the general population. This approach significantly enhances the feasibility, accessibility, and compliance of MASLD screening and provides an effective tool for large-scale health assessments and early intervention strategies.
Authors
- Na Yang (ORCID: https://orcid.org/0000-0002-3226-5631)
- Yipeng Song (ORCID: https://orcid.org/0000-0002-7180-2647)
- Liangjian Zheng
- Zenghong Lu (ORCID: https://orcid.org/0000-0003-1565-9708)
- Rui Xu (ORCID: https://orcid.org/0000-0002-8040-2768)
- Li‐Min Huang (ORCID: https://orcid.org/0000-0002-9291-260X)
- Xiangcai Wang (ORCID: https://orcid.org/0000-0002-7162-0552)
- Shi Geng (ORCID: https://orcid.org/0000-0001-9556-6012)
- Gangfeng Zhu
- Yi Xie
- Rui Zhu (ORCID: https://orcid.org/0000-0003-3242-6308)
- Yi Xiang (ORCID: https://orcid.org/0000-0002-8969-7371)
- Qiang Yi
- Xiaofei Feng (ORCID: https://orcid.org/0000-0003-1322-4102)
Institutions
- Xuzhou Medical College (CN)
- Zhongda Hospital Southeast University (CN)
- Gannan Medical University (CN)
- First Affiliated Hospital of Gannan Medical University (CN)
- Zhejiang University (CN)
- Southeast University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1186/s12967-025-06387-5
- Citations
- 20
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 27.08