Machine learning-based online prediction of nocturnal hypoglycemia in elderly patients with type 2 diabetes
The "RF-ET-KNN" model, integrating eleven clinically accessible features, effectively predicts NH in elderly T2D patients. Daytime lowest BG, FBG, and daytime hypoglycemia events were significant risk factors.
Authors
- Chenhua Guo
- Liang Zhao (ORCID: https://orcid.org/0000-0001-8197-6686)
- Rui Lin
- Xuhan Liu (ORCID: https://orcid.org/0000-0003-2368-4655)
- Xinyu Li (ORCID: https://orcid.org/0000-0001-7203-7417)
- Li Shen
- Bing Wang
- Yuntong Liu
- Zhengnan Gao
- Jingshi Wang
- Jiajun Huang
- Yan Zhu
- Jing Gao
- Yingshu Liu
Institutions
- Dalian Medical University (CN)
- Dalian University of Technology (CN)
- Dalian Municipal Central Hospital (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2026-01-07
- DOI
- https://doi.org/10.3389/fendo.2025.1685969
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Dalian Science and Technology Innovation Fund