Long-term Prediction of Severe Hypoglycemia in Type 2 Diabetes Based on Multi-view Co-training

Background Patients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, demanding urgent medical attention. Accurate prediction of SH remains challenging due to its multifactorial nature, contributed from factors such as medications, lifestyle choices, and metabolic measurements. Method In this study, we propose a systematic approach to improve the robustness and accuracy of SH predictions using machine learning models, guided by clinical feature selection. Our focus is on developing one-year SH prediction models using both semi-supervised learning and supervised learning algorithms. Utilizing the clinical trial, namely Action to Control Cardiovascular Risk in Diabetes, which involves electronic health records for over 10,000 individuals, we specifically investigate adults with T2DM who are at an increased risk of cardiovascular complications. Results Our results indicate that the application of a multi-view co-training method, incorporating the random forest algorithm, improves the specificity of SH prediction, while the same setup with Naive Bayes replacing random forest demonstrates better sensitivity. Our framework also provides interpretability of machine learning (XAI) models by identifying key predictors for hypoglycemia, including fast plasma glucose, hemoglobin A1c, general diabetes education, and NPH or L insulins. Conclusion By enhancing prediction accuracy and identifying crucial predictive features, our study contributes to advancing the understanding and management of hypoglycemia in this population.