Development and validation of a machine learning-based prognostic model for IgA nephropathy with CKD stage 3 or 4
Our model introduced seven risk factors that may be useful in predicting the progression of IgAN patients with CKD stage 3 or 4 and proteinuria ≥1.0 g/d. The RSF model is applicable for identifying the progression of IgAN and has outperformed than SSVM, XGboost, and Cox models.
Authors
- Xiaoxuan Ning (ORCID: https://orcid.org/0000-0002-5367-707X)
- Shiren Sun (ORCID: https://orcid.org/0000-0003-3345-3543)
- Yunlong Qin (ORCID: https://orcid.org/0000-0002-9442-4650)
- Zixian Yu (ORCID: https://orcid.org/0000-0002-0990-7691)
- Qing Jia (ORCID: https://orcid.org/0000-0002-7422-9671)
- Yumeng Zhang (ORCID: https://orcid.org/0000-0003-1942-1780)
- Zhao Jin (ORCID: https://orcid.org/0000-0001-9757-2352)
- Jinguo Yuan
- Yan Xing
Institutions
- Bethune International Peace Hospital (CN)
- Xijing Hospital (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Kidney Diseases
- Published
- 2024-08-22
- DOI
- https://doi.org/10.1159/000540682
- Citations
- 1
- Primary Topic
- Renal Diseases and Glomerulopathies
- Type
- article
- Field-Weighted Citation Impact
- 0.34
Funders
- National Natural Science Foundation of China
- Fourth Military Medical University