Development and validation of explainable machine learning models for predicting 3-month functional outcomes in acute ischemic stroke: a SHAP-based approach
Explainable machine learning models can accurately predict 3-month functional outcomes in AIS patients. The SHAP framework enhances model transparency, addressing interpretability barriers for clinical implementation while maintaining superior predictive performance.
Authors
- Chengfang Chen (ORCID: https://orcid.org/0000-0002-6568-0196)
- Huihua Zong
- Yi-tong Xiong
- Hong Yu (ORCID: https://orcid.org/0000-0002-1094-4334)
- Z. Z. Ren (ORCID: https://orcid.org/0009-0003-9743-8374)
Institutions
- Jiangsu University (CN)
- Yixing People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-12-02
- DOI
- https://doi.org/10.3389/fneur.2025.1678815
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Jiangsu University