Explainable cluster-based learning for prediction of postprandial glycemic events and insulin dose optimization in type 1 diabetes.

ABSTRACT: Effective management of postprandial glycemic excursions in type 1 diabetes requires accurate prediction of adverse events and personalized insulin adjustments informed by interpretable models. This study presents an explainable dual-prediction framework that simultaneously forecasts postprandial hypoglycemia and hyperglycemia within a 4-hour window using cluster-personalized ensemble models. Glycemic profiles were identified through a hybrid unsupervised approach combining self-organizing maps and k-means clustering, enabling the training of specialized random forest classifiers. The system outperformed baseline models on both real-world and simulated datasets, achieving high performance (AUC = 0.84 and 0.93; MCC = 0.47 and 0.73 for hypo- and hyperglycemia, respectively). Model interpreta

SUBMITTER: Rehman NU 

PROVIDER: S-EPMC12440209 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature