Nemat, H., Khadem, H., Eissa, M.R. orcid.org/0000-0002-5584-5815 et al. (2 more authors) (2022) Blood glucose level prediction : advanced deep-ensemble learning approach. IEEE Journal of Biomedical and Health Informatics, 26 (6). pp. 2758-2769. ISSN 2168-2194
Abstract
Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Blood glucose level; Deep learning; Diabetes mellitus; Meta-learning; Ensemble learning; Time series forecasting |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Funding Information: | Funder Grant number Economic and Social Research Council ES/V009796/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Jan 2022 10:59 |
Last Modified: | 25 Jan 2023 01:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/JBHI.2022.3144870 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183021 |