Explainable machine learning prediction of 1-year kidney function progression among patients with type 2 diabetes mellitus and chronic kidney disease: a retrospective study
Our findings suggest that the XGBoost model may serve as a valuable decision-support tool for predicting kidney function decline in patients with T2DM and CKD.
Authors
- Honglan Wei (ORCID: https://orcid.org/0000-0002-2707-1699)
- Ming Tian (ORCID: https://orcid.org/0000-0002-0945-1516)
- Shuangping Tan
- Junwu Dong (ORCID: https://orcid.org/0000-0002-1348-7212)
- Jinyi Wu (ORCID: https://orcid.org/0000-0002-1601-1084)
- Qi Gao (ORCID: https://orcid.org/0000-0002-7794-9869)
Institutions
- Wuhan Puai Hospital (CN)
- Shanghai Public Health Clinical Center (CN)
Publication Details
- Journal
- QJM
- Published
- 2025-04-23
- DOI
- https://doi.org/10.1093/qjmed/hcaf101
- Citations
- 1
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 1.43