<p>Basic characteristics of the included literature.</p>

Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST).</p> <p>Results</p><p>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.</p> <p>Conclusion</p><p>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.</p></div>