XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study
Our study highlights the usefulness of machine learning in unveiling important factors that influence the diagnosis of KOA to guide new prevention strategies. Further work is needed to validate this approach.
Authors
- Zijuan Fan (ORCID: https://orcid.org/0009-0001-4765-8067)
- Yuqing Zhang (ORCID: https://orcid.org/0000-0001-7954-1149)
- Jiao Jiao Li (ORCID: https://orcid.org/0000-0002-3584-6765)
- Wenzhu Song (ORCID: https://orcid.org/0000-0002-2877-4716)
- Ludan Jia
- Jianhao Lin (ORCID: https://orcid.org/0000-0003-1830-9244)
- Songyan Li (ORCID: https://orcid.org/0000-0001-9436-6705)
- Bin Wang (ORCID: https://orcid.org/0000-0002-5474-1002)
- Yan Ke (ORCID: https://orcid.org/0000-0002-1534-0011)
Institutions
- University of Technology Sydney (AU)
- Harvard University (US)
- Sun Yat-sen University (CN)
- Peking University (CN)
- Peking University People's Hospital (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
- Xinjiang University (CN)
Publication Details
- Journal
- Arthritis Research & Therapy
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1186/s13075-024-03450-2
- Citations
- 13
- Primary Topic
- Osteoarthritis Treatment and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 6.11
Funders
- National Natural Science Foundation of China
- Zhejiang University
- Natural Science Foundation of Zhejiang Province