Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features
We developed an explainable machine learning model to predict SO in aging community and nursing populations. This model offers a novel, accessible, and interpretable approach to SO prediction with potential to enhance early detection and intervention strategies. Further studies are warranted to validate our model in diverse populations and evaluate its impact on patient outcomes when integrated into comprehensive geriatric assessments.
Authors
- Huiyu Tang (ORCID: https://orcid.org/0000-0002-1111-1908)
- Zecong Chen
- Ming Yang (ORCID: https://orcid.org/0000-0002-1875-1692)
- Rongna Lian
- Xiaohong Chen (ORCID: https://orcid.org/0000-0002-9797-8384)
- Wenhua Jiang (ORCID: https://orcid.org/0000-0002-6252-6732)
- Shuyue Luo
- Jiaojiao Jiang
Institutions
- Sichuan University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- Aging Clinical and Experimental Research
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1007/s40520-025-02975-z
- Citations
- 8
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 9.31
Funders
- Health and Family Planning Commission of Sichuan Province