Authors : Jagruti Prajapati; Dr. Keyur Brahmbhatt

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/24ve4xfs

DOI : https://doi.org/10.38124/ijisrt/25jun1529

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Proactive glucose level prediction offers a vital advantage in the daily management of Type-1 diabetes, where unexpected fluctuations can lead to dangerous hypoglycemic or hyperglycemic events. This work introduces a Bi-LSTM- based deep learning model tailored for multivariate time series forecasting, targeting 30-minute and 60-minute blood glucose prediction horizons. Unlike univariate models, our approach incorporates multiple physiological signals—such as CGM values, insulin dosages (basal and bolus), and carbohydrate consumption—to capture the underlying temporal and causal relationships affecting glucose regulation. The model is trained on the OhioT1DM dataset, which comprises high-resolution (5-minute interval) data from 12 Type-1 diabetic subjects. The bidirectional architecture enables the model to process sequential patterns in both forward and backward directions, improving its sensitivity to evolving trends and sharp variations. Evaluation results highlight the model's ability to deliver accurate short- and mid-term predictions, thus supporting timely therapeutic actions and personalized diabetes care.

Keywords : Diabetes Management; Blood Glucose Prediction; CGM Devices; Deep Neural Network, BiLSTM.

References :

1. D. Magliano and E. J. Boyko, IDF diabetes atlas, 10th edition. Brussels:  International Diabetes Federation, 2021.
2. A. Bhimireddy, P. Sinha, B. Oluwalade, J. W. Gichoya, and S. Purkayastha, “Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks”.
3. T. Zhu, K. Li, P. Herrero, and P. Georgiou, “Deep Learning for Diabetes: A Systematic Review,” IEEE J. Biomed. Health Inform., vol. 25, no. 7, pp. 2744–2757, Jul. 2021, doi: 10.1109/JBHI.2020.3040225.
4. C. Marling and R. Bunescu, “The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020”.
5. M. De Bois, M. A. E. Yacoubi, and M. Ammi, “GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes,” Med Biol Eng Comput, vol. 60, no. 1, pp. 1–17, Jan. 2022, doi: 10.1007/s11517-021-02437-4.
6. K. Li, C. Liu, T. Zhu, P. Herrero, and P. Georgiou, “GluNet: A Deep Learning Framework for Accurate Glucose Forecasting,” IEEE J. Biomed. Health Inform., vol. 24, no. 2, pp. 414–423, Feb. 2020, doi: 10.1109/JBHI.2019.2931842.
7. Q. Sun, M. V. Jankovic, L. Bally, and S. G. Mougiakakou, “Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network,” in 2018 14th Symposium on Neural Networks and Applications (NEUREL), Belgrade: IEEE, Nov. 2018, pp. 1–5. doi: 10.1109/NEUREL.2018.8586990.
8. S. Mirshekarian, H. Shen, R. Bunescu, and C. Marling, “LSTMs and Neural Attention Models for Blood Glucose Prediction: Comparative Experiments on Real and Synthetic Data,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany: IEEE, Jul. 2019, pp. 706–712. doi: 10.1109/EMBC.2019.8856940.
9. J. Martinsson, A. Schliep, B. Eliasson, C. Meijner, S. Persson, and O. Mogren, “Automatic blood glucose prediction with confidence using recurrent neural networks”.
10. Q. Sun, M. V. Jankovic, L. Bally, and S. G. Mougiakakou, “Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network,” in 2018 14th Symposium on Neural Networks and Applications (NEUREL), Belgrade: IEEE, Nov. 2018, pp. 1–5. doi: 10.1109/NEUREL.2018.8586990.
11. A. Z. Woldaregay et al., “Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes,” Artificial Intelligence in Medicine, vol. 98, pp. 109–134, Jul. 2019, doi: 10.1016/j.artmed.2019.07.007.
12. M. F. Rabby, Y. Tu, M. I. Hossen, I. Lee, A. S. Maida, and X. Hei, “Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction,” BMC Med Inform Decis Mak, vol. 21, no. 1, p. 101, Dec. 2021, doi: 10.1186/s12911-021-01462-5.
13. S. Hochreiter and J. Schmidhuber, "Long Short-term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1740, 1997, doi: 10.1162/neco.1997.9.8.1735.
14. C. Olah, "Understanding LSTM Networks," colah.github.io, https://colah.github.io/posts/2015-08-Understanding-LSTMs/, Accessed: October 26, 2022.
15. H. Rubin-Falcone, I. Fox, and J. Wiens, “Deep residual time-series forecasting: Application to blood glucose prediction,” in The 5th International Workshop on Knowledge Discovery in Healthcare Data, ECAI, 2020, pp. 111–116.
16. H. Hameed and S. Kleinberg, “Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting”. in The 5th International Workshop on Knowledge Discovery in Healthcare Data, ECAI, 2020, pp. 91–95.
17. T. Zhu, X. Yao, K. Li, P. Herrero, and P. Georgiou, “Blood Glucose Prediction for Type 1 Diabetes Using Generative Adversarial Networks”. in The 5th International Workshop on Knowledge Discovery in Healthcare Data, ECAI, 2020, pp. 96–100.
18. T. Yang et al., “Multi-Scale Long Short-Term Memory Network with Multi-Lag Structure for Blood Glucose Prediction”. in The 5th International Workshop on Knowledge Discovery in Healthcare Data, ECAI, 2020, pp. 142–146.
19. R. Bevan and F. Coenen, “Experiments in non-personalized future blood glucose level prediction”. in The 5th International Workshop on Knowledge Discovery in Healthcare Data, ECAI, 2020, pp. 106–110.
20. X. Sun et al., “Prediction of Blood Glucose Levels for People with Type 1 Diabetes using Latent-Variable-based Model”. in The 5th International Workshop on Knowledge Discovery in Healthcare Data, ECAI, 2020, pp. 121–125.
21. M. M. H. Shuvo, T. Titirsha, K. S. M. T. Hossain, G. L. Gonzalez, and S. K. Islam, “Enhancing Personalization and Mitigating Inter-Patient Variability in Continuous Blood Glucose Prediction Using Multi-Task Deep LSTMs,” in 2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Eindhoven, Netherlands: IEEE, Jun. 2024, pp. 1–6. doi: 10.1109/MeMeA60663.2024.10596811.
22. T. Zhu, T. Chen, L. Kuangt, J. Zeng, K. Li, and P. Georgiou, “Edge-Based Temporal Fusion Transformer for Multi-Horizon Blood Glucose Prediction,” in 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, USA: IEEE, May 2023, pp. 1–5. doi: 10.1109/ISCAS46773.2023.10181448.