Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting.

GlucoDiaNet achieved a RMSE of 5.2435 mg/dL for blood glucose prediction at 30 minutes, indicating strong predictive performance, but further validation is needed before clinical application.

Summary and findings

This study evaluated a novel framework, Glucose Dynamics Analysis Network (GlucoDiaNet), for predicting blood glucose (BG) levels using the OhioT1DM dataset. The model achieved a Root Mean Squared Error (RMSE) of 5.2435 mg/dL at a 30-minute prediction horizon. No therapeutic claims are made regarding its application in clinical settings.

Abstract

<h4>Background</h4>Accurate real-time prediction of blood glucose (BG) levels is essential for improving insulin-dosing decision support systems, including closed-loop insulin delivery and bolus calculators. However, existing deep learning models often suffer from high computational complexity, limited utilization of physiological factors, and inadequate handling of temporal glucose dependencies.<h4>Methods</h4>This study proposes Glucose Dynamics Analysis Network (GlucoDiaNet), a hybrid framework for BG prediction integrating spline interpolation for missing value handling, a Dilated Convolutional Residual Network (DilaConv-ResNet) for spatial-temporal feature extraction, Adamax optimization for feature selection and hyperparameter tuning, and a Bidirectional Long Short-Term Memory network for bidirectional sequence learning. The model was evaluated using the OhioT1DM dataset across multiple prediction horizons ranging from 30 to 60 min.<h4>Results</h4>At the 30-min prediction horizon, GlucoDiaNet achieved a Root Mean Squared Error (RMSE) of 5.2435 mg/dL, Mean Absolute Error (MAE) of 4.3622 mg/dL, R ² value of 0.9948, and Mean Squared Error (MSE) of 29.3056. The proposed model consistently outperformed baseline models including LSTM, GRU, and TCN across both short- and long-term forecasting tasks while maintaining robust predictive performance at extended prediction intervals.<h4>Conclusion</h4>GlucoDiaNet effectively enhances blood glucose prediction by integrating efficient preprocessing, deep temporal modeling, and optimization strategies. The proposed framework demonstrates strong potential for future deployment in real-time and wearable diabetes monitoring systems, subject to further hardware-level validation and computational efficiency analysis.

Key findings

• Not reported in abstract.

Limitations

• Not reported in abstract.

Elsewhere in the Adamax corpus