Machine learning-based prediction of hypoglycemia severity in hospitalized diabetic patients - Jia, Hongjian; Zhang, Jietao (Frontiers in Endocrinology, 2025)
ObjectiveTo identify risk factors for hypoglycemia in hospitalized patients with type 2 diabetes mellitus (T2DM) and develop predictive models for hypoglycemia severity based on machine learning algorithms.MethodsAdult non-pregnant hospitalized patients diagnosed with T2DM were retrospectively enrolled from the electronic medical record system of the Affiliated Hospital of Qingdao University. Patients were categorized into hypoglycemia groups (mild, moderate-to-severe) or a non-hypoglycemia group based on inpatient venous plasma glucose levels. After data preprocessing, univariate and multivariate analyses were conducted to identify significant predictors. Three predictive models (XGBoost, Random Forest [RF], and Logistic Regression) were subsequently constructed and validated to evaluate their predictive performances.ResultsFrom an initial cohort of 8,947 patients, 1,798 patients were included after data screening. Among the evaluated models, the RF model demonstrated the highest predictive accuracy (93.3%) and Kappa coefficient (0.873), followed by XGBoost (accuracy: 92.6%, Kappa: 0.860). Logistic regression exhibited comparatively lower performance (accuracy: 83.8%, Kappa: 0.685). The macro-average area under the ROC curve (AUC) values for RF, XGBoost, and logistic regression were 0.960, 0.955, and 0.788, respectively, highlighting the superior discriminative capability of the RF model. While both XGBoost and RF models identified glycemic control metrics and glucose variability as core predictors for hypoglycemia, the RF model additionally emphasized medication usage, whereas XGBoost prioritized basal metabolic parameters.ConclusionsThe RF model outperformed XGBoost and conventional logistic regression in predicting hypoglycemia severity among hospitalized T2DM patients. The results emphasize the importance of closely monitoring glucose levels and glucose variability during diabetes management to prevent hypoglycemia. The developed model provides a foundation for implementing preventive strategies to reduce hypoglycemia occurrence in hospitalized patients with T2DM.