setsci

SETSCI Conference Proceedings

2687-5527

SET Technology

10.36287/setsci.22.4.001

ecf6d03b-7d6d-4285-bbe7-898e4f4c6421

Explainable Multi-Task Deep Learning for Blood Glucose Forecasting: A Lightweight, Interpretability Focused Approach

Maqsood

Sarmad

Sarwar

Muhammad Abdullah

Belousoviene

Egle

Maskeliünas

Rytis

Department of Applied Informatics, Vytautas Magnus University Centre of Real Time Computer Systems, Faculty of Informatics, Kaunas University of Technology Department of Intensive Care, University of Health Sciences

10 07 2025

22 1 6

2025

SET Technology

CC BY 4.0

Accurate and transparent blood glucose forecasting is crucial for effective diabetes management. This paper presents an interpretable deep learning (DL) framework based on multi-task learning (MTL) for forecasting glucose levels at multiple prediction horizons. In contrast to complex hybrid systems, we isolate the MTL core and integrate explainability methods such as Shapley Additive Explanations (SHAP) and permutation-based feature importance (PFI). Our approach enables a clear understanding of model behavior while achieving strong predictive performance. Evaluated on the BrisT1D dataset, the model achieves an R^2 score of 0.956, RMSE of 0.045, and MAE of 0.033, while highlighting the critical influence of historical glucose readings. This focused study provides insights into how interpretable AI can support reliable decision-making in diabetes care.

Blood glucose forecasting multi-task learning explainable AI attention mechanism diabetes prediction

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