A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus
The developed XGBoost model demonstrated optimal predictive accuracy for the occurrence of DKD in patients with T2DM. This model facilitated the construction of an online prediction calculator, offering an accessible and practical tool for both patients and clinicians.
Authors
- Xiaohua Liang (ORCID: https://orcid.org/0000-0003-3867-9779)
- Yang Yang (ORCID: https://orcid.org/0000-0003-2238-6437)
- Yi Cao (ORCID: https://orcid.org/0000-0002-8262-5236)
- S. Wang
- Xiao-Yan He
- Xin Zhang (ORCID: https://orcid.org/0000-0003-4099-6175)
- Tingting Li (ORCID: https://orcid.org/0000-0002-5011-0174)
- Zhen Xu
- Jinbo Chen (ORCID: https://orcid.org/0000-0001-5637-0922)
- Dong Ma (ORCID: https://orcid.org/0000-0003-4548-1517)
- Kaiwen Wang (ORCID: https://orcid.org/0000-0003-2881-8181)
- Xiaolu Cao (ORCID: https://orcid.org/0000-0002-8494-2913)
- Xuesen Zhao
- P. P. Su (ORCID: https://orcid.org/0000-0002-8028-3607)
Institutions
- Hebei University of Engineering (CN)
- Hebei Medical University (CN)
- Ministry of Education of the People's Republic of China (CN)
- North China University of Science and Technology (CN)
- Zhengzhou University (CN)
- First Hospital of Shijiazhuang (CN)
- Hebei Eye Hospital (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-10-15
- DOI
- https://doi.org/10.3389/fendo.2025.1587932
- Citations
- 4
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 11.75
Funders
- National Natural Science Foundation of China
- Department of Education of Hebei Province
- Natural Science Foundation of Hebei Province