Exploring the Influence of Patients’ Heterogeneity on Automated Prediction of Blood Glucose Levels in Type 1 Diabetes Baños Legrán, Oresti Avilés-Pérez, María Dolores Gayaroa, Álvaro Lopez-Ibarra Lozano, Pablo J. Muñoz Torres, Manuel Eduardo Quesada Charneco, Miguel Rodríguez León, Ciro Villalonga Palliser, Claudia This work has been supported by the PID2023-148188OA-I00 project ”RELIEF-T1D” which is funded by MICIU/AEI/10.13039/501100011033 and ERDF EU. This work investigates the impact of patients’ heterogeneity, including factors such as gender, age, and clinical conditions, on the performance of machine learning models in predicting blood glucose levels in individuals with Type 1 Diabetes. Using the T1DiabetesGranada dataset, various datasets were generated based on these heterogeneity factors. Three popular prediction models —a Linear model, an LSTM neural network, and a CNN— were applied to both the generated datasets and the complete dataset to measure prediction performance at a 30-minute prediction horizon. The preliminary results suggest that incorporating patient-specific heterogeneity factors generally improves prediction performance, highlighting the existence of bias in standard blood glucose level prediction models. Future research should explore whether these findings hold in other related datasets. 2026-03-11T12:36:00Z 2026-03-11T12:36:00Z 2025-12-03 conference output Baños Legrán, O.; Avilés-Pérez, M. D.; Gayaroa, A. [et al]. (2025). Exploring the Influence of Patients’ Heterogeneity on Automated Prediction of Blood Glucose Levels in Type 1 Diabetes. In 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark, pp. 1-5, doi: 10.1109/EMBC58623.2025.11253928 979-8-3315-8618-8 2694-0604 https://hdl.handle.net/10481/112035 10.1109/EMBC58623.2025.11253928 eng open access IEEE