Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a cross-sectional study
This study is the first to predict sleep disorders in Chinese older adults with multimorbidity using explainable machine learning methods and to identify seven significant risk factors. The SHAP method enhances the interpretability of machine learning models and helps medical staff better understand the rationale behind the predicted outcomes more effectively.
Authors
- Yu Qian (ORCID: https://orcid.org/0000-0003-3991-0880)
- Ru Gao (ORCID: https://orcid.org/0000-0001-5706-2032)
- Sha Meng (ORCID: https://orcid.org/0009-0000-5206-0900)
- Zhao Jiang (ORCID: https://orcid.org/0000-0001-8686-2784)
- Zeng Li (ORCID: https://orcid.org/0000-0002-7088-5524)
- Lu Liu (ORCID: https://orcid.org/0000-0001-8338-1168)
- Xia Wang (ORCID: https://orcid.org/0000-0001-5655-2291)
- Rong Zhang (ORCID: https://orcid.org/0000-0002-9815-2832)
- Dan Zhang (ORCID: https://orcid.org/0000-0003-1295-4795)
- Yu Zeng (ORCID: https://orcid.org/0000-0003-3550-3495)
- Yong Li (ORCID: https://orcid.org/0000-0002-3098-6363)
- Jingjie Zhou (ORCID: https://orcid.org/0009-0004-0165-1339)
Institutions
- Chengdu Medical College (CN)
- Chengdu University (CN)
- Zigong First People's Hospital (CN)
- Second People’s Hospital of Yibin (CN)
- Dazhou Central Hospital (CN)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-08-11
- DOI
- https://doi.org/10.3389/fpubh.2025.1619406
- Citations
- 1
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Department of Science and Technology of Sichuan Province