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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2020: Biomedical Engineering Systems and Technologies pp 347–365Cite as

Evaluating a Comparing Deep Learning Architectures for Blood Glucose Prediction

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1400))

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.

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Author information

Authors and Affiliations

  1.  Department of Computer Sciences EMI, University Mohamed V, Rabat, Morocco

    Touria El Idrissi

  2. Software Project Management Research Team, RITC, ENSIAS, University Mohamed V, Rabat, Morocco

    Ali Idri

  3. Complex Systems Engineering, University Mohammed VI Polytechnic, Ben Guerir, Morocco

    Ali Idri

Authors
  1. Touria El Idrissi
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  2. Ali Idri
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Corresponding author

Correspondence to Ali Idri .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xuesong Ye

  2. Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal

    Filipe Soares

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. Universidad Politécnica de Madrid, Madrid, Spain

    Pedro Gómez Vilda

  5. University of Milano-Bicocca, Milan, Italy

    Federico Cabitza

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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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

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  • DOI: https://doi.org/10.1007/978-3-030-72379-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72378-1

  • Online ISBN: 978-3-030-72379-8

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