Performance of machine learning–based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis
- 1Department of General Practice, the People’s Hospital of Leshan, Leshan, China
Abstract
Objectives: This study aimed to systematically evaluate the predictive performance of machine learning (ML)–based models for predicting hypoglycemia in Chinese patients with diabetes. Methods: We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, CNKI, and Wanfang databases from inception to February 2026. Eligible studies focused on the development or validation of ML–based models for predicting hypoglycemia in Chinese patients with diabetes. Study selection and data extraction were performed independently by two reviewers. Information on study characteristics, modeling approaches, predictors, validation methods, and model performance was collected. The area under the receiver operating characteristic curve (AUC) was synthesized using a random-effects model. Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: A total of 13 studies were included, and the pooled prevalence of hypoglycemia was 25% (95% CI: 17%–33%). The overall pooled area under the receiver operating characteristic curve (AUC) was 0.90 (95% CI: 0.87–0.93). Subgroup analyses by modeling algorithms showed pooled AUCs of 0.89 for extreme gradient boosting (XGBoost), 0.88 for random forest (RF), 0.85 for support vector machine (SVM), 0.84 for Light Gradient Boosting Machine (LightGBM), 0.83 for logistic regression (LR), and 0.81 for decision tree (DT) models. Common predictors included age, insulin use, body mass index, HbA1c, creatinine, and history of hypoglycemia. Conclusion: We attempted to provide a comprehensive overview of machine learning–based prediction models for hypoglycemia in patients with diabetes. Research in this field remains at an early stage, although several models with good discriminatory performance have been reported. Methodological limitations and insufficient validation were observed in many studies. Concerns regarding model robustness and interpretability also exist. More efforts to develop reliable and interpretable models and to promote their application in clinical practice for early risk identification are needed.
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BibTeX
@article{yan2026performance,
title = {{Performance of machine learning–based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis}},
author = {Jinhua Yan and Yanping Song and Yangmei Du and Fanmin Li},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358876},
url = {https://doi.org/10.1371/journal.pone.0358876},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Performance of machine learning–based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis
AU - Jinhua Yan
AU - Yanping Song
AU - Yangmei Du
AU - Fanmin Li
PY - 2026
DA - 2026/09/22
JO - PLOS One
DO - 10.1371/journal.pone.0358876
UR - https://doi.org/10.1371/journal.pone.0358876
AB - Objectives: This study aimed to systematically evaluate the predictive performance of machine learning (ML)–based models for predicting hypoglycemia in Chinese patients with diabetes. Methods: We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, CNKI, and Wanfang databases from inception to February 2026. Eligible studies focused on the development or validation of ML–based models for predicting hypoglycemia in Chinese patients with diabetes. Study selection and data extraction were performed independently by two reviewers. Information on study characteristics, modeling approaches, predictors, validation methods, and model performance was collected. The area under the receiver operating characteristic curve (AUC) was synthesized using a random-effects model. Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: A total of 13 studies were included, and the pooled prevalence of hypoglycemia was 25% (95% CI: 17%–33%). The overall pooled area under the receiver operating characteristic curve (AUC) was 0.90 (95% CI: 0.87–0.93). Subgroup analyses by modeling algorithms showed pooled AUCs of 0.89 for extreme gradient boosting (XGBoost), 0.88 for random forest (RF), 0.85 for support vector machine (SVM), 0.84 for Light Gradient Boosting Machine (LightGBM), 0.83 for logistic regression (LR), and 0.81 for decision tree (DT) models. Common predictors included age, insulin use, body mass index, HbA1c, creatinine, and history of hypoglycemia. Conclusion: We attempted to provide a comprehensive overview of machine learning–based prediction models for hypoglycemia in patients with diabetes. Research in this field remains at an early stage, although several models with good discriminatory performance have been reported. Methodological limitations and insufficient validation were observed in many studies. Concerns regarding model robustness and interpretability also exist. More efforts to develop reliable and interpretable models and to promote their application in clinical practice for early risk identification are needed.
SN - 1932-6203
ER -