Explainable machine learning identifies key quality-of-life-related predictors of arthritis status: evidence from the China health and retirement longitudinal study
This study successfully applied an XAI pipeline to identify and rank key QoL-related factors predictive of arthritis status in a large Chinese cohort, achieving robust model performance. Pain burden, age, subjective health, sleep, functional status, and psychological well-being are critical determinants. These interpretable findings can inform risk stratification and guide targeted interventions focusing on these key areas to potentially improve arthritis management.
Authors
- Yiyue Chen (ORCID: https://orcid.org/0000-0001-5578-121X)
- Jiafen Liao (ORCID: https://orcid.org/0000-0003-2890-9382)
- Xi Xu (ORCID: https://orcid.org/0000-0002-8290-1827)
- Kaibin Lin (ORCID: https://orcid.org/0000-0003-1973-9537)
- Zheng Wang (ORCID: https://orcid.org/0000-0002-9330-0728)
- Xin Zhou (ORCID: https://orcid.org/0000-0003-4095-3503)
- Bing Zhou
- T. J. Jiang (ORCID: https://orcid.org/0000-0002-5039-6192)
Institutions
- Central South University (CN)
- Hunan University of Technology (CN)
- Hunan First Normal University (CN)
Publication Details
- Journal
- Health and Quality of Life Outcomes
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1186/s12955-025-02412-9
- Citations
- 1
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Natural Science Foundation of China
- Central South University
- Natural Science Foundation of Hunan Province