Development of an interpretable machine learning model for predicting sarcopenia in patients undergoing maintenance hemodialysis
This study developed a Logistic Regression model using an interpretable machine learning approach, offering an efficient tool for early screening of sarcopenia risk in MHD patients and facilitating personalized intervention strategies. However, the single-center design limits the model's external applicability, and further multi-center studies are necessary to validate its generalizability.
Authors
- Xisheng Xie (ORCID: https://orcid.org/0000-0002-2268-7662)
- Mi Li (ORCID: https://orcid.org/0000-0002-4886-2430)
- Zhixin Wang (ORCID: https://orcid.org/0000-0001-7255-6049)
- Shuqin Liu (ORCID: https://orcid.org/0000-0002-2831-6600)
- Ying Zhang (ORCID: https://orcid.org/0000-0001-9900-7568)
- Huaming Xian
- Xingyu Zhu (ORCID: https://orcid.org/0000-0002-0300-4782)
- Wenwu Tang (ORCID: https://orcid.org/0000-0002-5163-0737)
Institutions
- North Sichuan Medical University (CN)
- Taiyuan Central Hospital (CN)
- Nanchong Central Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-11-04
- DOI
- https://doi.org/10.3389/fmed.2025.1576081
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Sichuan Provincial Administration of Traditional Chinese Medicine