Data-Efficiency with Comparable Accuracy: Personalized LSTM Neural Network Training for Blood Glucose Prediction in Type 1 Diabetes Management

Background/Objectives: Accurate blood glucose forecasting is critical for closed-loop insulin delivery systems to support effective disease management in people with type 1 diabetes (T1D). While long short-term memory (LSTM) neural networks have shown strong performance in glucose prediction tasks, the relative performance of individualized versus aggregated training remains underexplored. Methods: In this study, we compared LSTM models trained on individual-specific data to those trained on aggregated data from 25 T1D subjects using the HUPA UCM dataset. Results: Despite having access to substantially less training data, individualized models achieved comparable prediction accuracy to aggregated models, with mean root mean squared error across 25 subjects of 22.52 ± 6.38 mg/dL for the individualized models, 20.50 ± 5.66 mg/dL for the aggregated models, and Clarke error grid Zone A accuracy of 84.07 ± 6.66% vs. 85.09 ± 5.34%, respectively. Subject-level analyses revealed only modest differences between the two approaches, with some individuals benefiting more from personalized training. Conclusions: These findings suggest that accurate and clinically reliable glucose prediction is achievable using personalized models trained on limited individual data, with important implications for adaptive, on-device training, and privacy-preserving applications.