ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Clinical Diabetes
This article is part of the Research TopicDigital Technology in the Management and Prevention of Diabetes: Volume IIIView all 12 articles
Machine Learning-Based Online Prediction of Nocturnal Hypoglycemia in Elderly Patients with Type 2 Diabetes
Provisionally accepted
Yingshu Liu1*
Yuntong Liu1Chenhua Guo2Xinyu Li1Shen Li1Jiajun Huang2
Liang Zhao2Yan Zhu1
Xuhan Liu1Bing Wang1Rui Lin2Jingshi Wang3Zhengnan Gao1
Jing Gao2- 1Central Hospital of Dalian University of Technology, Dalian, China
- 2Dalian University of Technology, Dalian, China
- 3Dalian Women and Children's Medical Center, Dalian, China
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Context Nocturnal hypoglycemia (NH) is a common adverse event in elderly patients with type 2 diabetes (T2D). This study aims to develop a clinically applicable model for predicting the risk of NH in elderly patients with T2D. Methods This retrospective cohort study, conducted from May 2018 to June 2024, analyzed 1,128 elderly T2D patients undergoing continuous glucose monitoring, with an independent validation involving 100 outpatients. Clinical characteristics were collected, and feature engineering was performed to select a manageable set of clinically accessible features. An ensemble model was developed using multiple base models and a stacking approach. The best-performing model was deployed as an online risk calculator. Results Of the development set, 288 (25.5%) experienced NH, while 40 (40%) of the independent validation cohort experienced NH. The final ensemble model, "RF-ET-KNN", combined random forest, Extra Trees, and K-nearest neighbor as base learners, with Extra Trees serving as the meta-learner. It incorporated eleven clinical features and achieved an AUROC of 0.926 and sensitivity of 0.853 on the test set, and an AUROC of 0.947 and sensitivity of 0.929 on the internal validation set. SHAP analysis identified that daytime lowest blood glucose (BG), fasting blood glucose (FBG), and daytime hypoglycemia events were closely related to NH. A user-friendly calculator is available at http://122.51.219.102:8000/. Conclusion 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.
Keywords: Nocturnal hypoglycemia, type 2 diabetes, ensemble learning, elderly people, Clinical prediction model
Received: 14 Aug 2025; Accepted: 10 Dec 2025.
Copyright: © 2025 Liu, Liu, Guo, Li, Li, Huang, Zhao, Zhu, Liu, Wang, Lin, Wang, Gao and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yingshu Liu
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