Machine Learning Models for Identifying the Risk of Chronic Kidney Disease in Patients with Coronary Heart Disease: A Retrospective Study
The XGBoost model constructed on routine clinical data was effective in identifying CKD risk in CHD patients, with UAR as a novel strong predictor. Decision curve analysis confirmed the clinical utility of the model, indicating that it may be used to guide decisions for enhanced monitoring and early intervention over a wide range of risk thresholds.
Authors
- Ling Hou (ORCID: https://orcid.org/0000-0001-6748-6314)
- Ke Su (ORCID: https://orcid.org/0000-0002-7948-6335)
- Ting He (ORCID: https://orcid.org/0000-0002-6653-6356)
- Yuanhong Li (ORCID: https://orcid.org/0000-0002-2215-3636)
- Jinbo Zhao (ORCID: https://orcid.org/0000-0002-7806-6072)
Institutions
- The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture (CN)
Publication Details
- Journal
- International Journal of General Medicine
- Published
- 2025-12-01
- DOI
- https://doi.org/10.2147/ijgm.s558568
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 0.00