Prediction of ACL injury incidence and analysis of key features in basketball players based on multi-algorithm models
The random forest model demonstrated superior predictive performance, providing valuable insights into the key risk factors associated with ACL injury among male basketball players. This study highlighted the importance of biomechanical testing based on sport-specific movements to accurately predict the ACL injury risk.
Authors
- Zhilei Cui
- Wei Ping Loh (ORCID: https://orcid.org/0000-0002-9633-3410)
- Shazlin Shaharudin (ORCID: https://orcid.org/0000-0001-5583-7631)
- Longfei Guo (ORCID: https://orcid.org/0009-0005-2098-2413)
Institutions
- Universiti Sains Malaysia (MY)
- Hospital Universiti Sains Malaysia (MY)
- Jinzhong University (CN)
- Taiyuan Institute of Technology (CN)
- Taiyuan University of Science and Technology (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2025-10-14
- DOI
- https://doi.org/10.7717/peerj.20141
- Primary Topic
- Knee injuries and reconstruction techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00