Abstract
To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this paper, we conduct a comparative study of two emerging deep learning techniques: Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for one-step and multi-steps-ahead forecasting of the BGL based on Continuous Glucose Monitoring (CGM) data. The objectives are twofold: 1) Determining the best strategies of multi-steps-ahead forecasting (MSF) to fit the CNN and LSTM models respectively, and 2) Comparing the performances of the CNN and LSTM models for one-step and multi-steps prediction. Toward these objectives, we firstly conducted series of experiments of a CNN model through parameters selection to determine its best configuration. The LSTM model we used in the present study was developed and evaluated in an earlier work. Thereafter, five MSF strategies were developed and evaluated for the CNN and LSTM models using the Root-Mean-Square Error (RMSE) with an horizon of 30 min. To statistically assess the differences between the performances of CNN and LSTM models, we used the Wilcoxon statistical test. The results showed that: 1) no MSF strategy outperformed the others for both CNN and LSTM models, and 2) the proposed CNN model significantly outperformed the LSTM model for both one-step and multi-steps prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bilous, R., Donnelly, R.: Handbook of Diabetes. Wiley, Hoboken (2010)
El Idrissi, T., Idri, A., Bakkoury, Z.: Systematic map and review of predictive techniques in diabetes self-management. Int. J. Inf. Manag. 46, 263–277 (2019)
El Idrissi, T., Idri, A., Abnane, I., Bakkoury, Z.: Predicting blood glucose using an LSTM neural network. In: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems. ACSIS, vol. 18, pp. 35–41 (2019)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067–7083 (2012)
An, N.H., Anh, D.T.: Comparison of strategies for multi-step-ahead prediction of time series using neural network. In: 2015 International Conference on Advanced Computing and Applications (ACOMP), pp. 142–149. IEEE, November 2015
El Idrissi, T., Idri, A., Kadi, I., Bakkoury, Z.: Strategies of multi-step-ahead forecasting for blood glucose level using LSTM neural networks: a comparative study. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pp. 337–344 (2020)
Xie, J., Wang, Q.: Benchmark machine learning approaches with classical time series approaches on the blood glucose level prediction challenge. In: KHD@ IJCAI, pp. 97–102, January 2018
Fox, I., Ang, L., Jaiswal, M., Pop-Busui, R., Wiens, J.: Deep multi-output forecasting: learning to accurately predict blood glucose trajectories. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1387–1395. ACM, July 2018
Kline, D.M.: Methods for multi-step time series forecasting neural networks. In: Neural Networks in Business Forecasting, pp. 226–250. IGI Global (2004)
Sorjamaa, A., Lendasse, A.: Time series prediction using DirRec strategy. In: ESANN, vol. 6, pp. 143–148, April 2006
Taieb, S.B., Bontempi, G., Sorjamaa, A., Lendasse, A.: Long-term prediction of time series by combining direct and mimo strategies. In: 2009 International Joint Conference on Neural Networks, pp. 3054–3061. IEEE, June 2009
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Fukushima, K., Miyake, S.: Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recogn. 15(6), 455–469 (1982)
Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962)
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 396–404 (1990)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Min. Knowl. Discov. 33(4), 917–963 (2019)
Li, K., Liu, C., Zhu, T., Herrero, P., Georgiou, P.: GluNet: A deep learning framework for accurate glucose forecasting. IEEE J. Biomed. Health Inform. 24, 414–423 (2019)
Woldaregay, A.Z., et al.: Data-driven modeling and prediction of blood glucose dynamics: machine learning applications in type 1 diabetes. In: Artificial Intelligence in Medicine (2019)
El Idrissi, T., Idri, A., Bakkoury, Z.: Data mining techniques in diabetes self-management: a systematic map. In: 6th World Conference on Information Systems and Technologies, Naple, pp. 1142–1152, March 2018
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations https://arxiv.org/abs/1409.1556 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems 25, pp. 1090–1098 (2012)
Girshick, R., Donahue, J., Darrell, T. Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Sig. Process. Mag. 29, 82–97 (2012)
Sun, Q., Jankovic, M.V., Bally, L., Mougiakakou, S.G.: Predicting blood glucose with an LSTM and Bi-LSTM based deep neural network. In: 2018 14th Symposium on Neural Networks and Applications (NEUREL), pp. 1–5. IEEE, November 2018
Zhu, T., Li, K., Herrero, P., Chen, J., Georgiou, P.: A deep learning algorithm for personalized blood glucose prediction. In: KHD@ IJCAI, pp. 64–78 (2018)
Mhaskar, H.N., Pereverzyev, S.V., van der Walt, M.D.: A deep learning approach to diabetic blood glucose prediction. Front. Appl. Math. Stat. 3, 14 (2017)
Mirshekarian, S., Bunescu, R., Marling, C., Schwartz, F.: Using LSTMs to learn physiological models of blood glucose behavior. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2887–2891. IEEE, July 2017
Doike, T., Hayashi, K., Arata, S., Mohammad, K.N., Kobayashi, A., Niitsu, K.: A blood glucose level prediction system using machine learning based on recurrent neural network for hypoglycemia prevention. In: 2018 16th IEEE International New Circuits and Systems Conference (NEWCAS), pp. 291–295. IEEE, June 2018
DirecNet: Diabetes Research in Children Network (2019). https://direcnet.jaeb.org/Studies.aspx. Accessed 1 Apr 2019
Idri, A., Abnane, I., Abran, A.: Missing data techniques in analogy-based software development effort estimation. J. Syst. Softw. 117, 595–611 (2016)
Héberger, K.: Sum of ranking differences compares methods or models fairly. TrAC Trends Anal. Chem. 29(1), 101–109 (2010)
Hosni, M., Idri, A., Abran, A.: Investigating heterogeneous ensembles with filter feature selection for software effort estimation. In: Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement, pp. 207–220, October 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
El Idrissi, T., Idri, A. (2021). Evaluating a Comparing Deep Learning Architectures for Blood Glucose Prediction. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-72379-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72378-1
Online ISBN: 978-3-030-72379-8
eBook Packages: Computer ScienceComputer Science (R0)