Machine learning-based risk prediction model for arteriovenous fistula stenosis
The XGBoost model effectively predicts arteriovenous fistula stenosis risk using clinical data. SHAP explanations enhance clinical interpretability, aiding personalized care strategies.
Authors
- Shanshan Huo (ORCID: https://orcid.org/0000-0001-7846-1198)
- Peng Shu (ORCID: https://orcid.org/0000-0001-8945-8402)
- Jun Qiu (ORCID: https://orcid.org/0000-0002-2733-7979)
- Xia Wang (ORCID: https://orcid.org/0000-0001-5655-2291)
- Haitao Bai (ORCID: https://orcid.org/0000-0002-8348-4164)
- Fang Xu (ORCID: https://orcid.org/0000-0002-2326-6288)
- Ling Huang (ORCID: https://orcid.org/0000-0002-0999-4326)
Institutions
- Central Hospital of Wuhan (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-03-29
- DOI
- https://doi.org/10.1186/s40001-025-02490-x
- Citations
- 4
- Primary Topic
- Central Venous Catheters and Hemodialysis
- Type
- article
- Field-Weighted Citation Impact
- 10.07