Bayesian Neural Network for Uncertainty-Aware Blood Glucose Prediction for Type 1 Diabetes

Accurate blood glucose prediction is vital for effective diabetes management. However, traditional models focus primarily on improving accuracy, often overlooking the importance of quantifying uncertainty, which is essential for informed clinical decision-making. This study presents an uncertainty-aware blood glucose prediction model using a Bayesian neural network, which not only focuses on predictive accuracy but also emphasizes uncertainty analysis. The model is trained on blood glucose data and evaluated using Root Mean Squared Error (RMSE) for accuracy and confidence interval coverage to assess uncertainty calibration. The results show that the model achieves an RMSE of 22.21, indicating strong predictive performance. Moreover, the observed glucose values consistently fall within the predicted confidence intervals <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\pm 2 \sigma)$</tex>, demonstrating well-calibrated uncertainty estimates. The low correlation (0.06) between glucose rate of change and uncertainty further suggests that the model performs robustly across both stable and fluctuating glucose levels.