Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI Article Swipe
Reem Alkanhel
,
Hager Saleh
,
Ahmed Elaraby
,
Saleh Alharbi
,
Hela Elmannai
,
Saad Alaklabi
,
Saeed Hamood Alsamhi
,
Sherif Mostafa
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237670
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237670
For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. To anticipate BGL many steps ahead, we propose a novel hybrid deep learning model framework based on Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs), which can be integrated into the Internet of Things (IoT)-enabled diabetes management systems, improving prediction accuracy and timeliness by allowing real-time data processing on edge devices. While the GRU layer records temporal relationships and sequence information, the CNN layer analyzes the incoming data to extract significant features. Using a publicly accessible type 1 diabetes dataset, the hybrid model’s performance is compared to that of the standalone Long Short-Term Memory (LSTM), CNN, and GRU models. The findings show that the hybrid CNN-GRU model performs better than the single models, indicating its potential to significantly improve real-time BGL forecasting in IoT-based diabetes management systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24237670
- OA Status
- gold
- Cited By
- 5
- References
- 38
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405002166Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24237670Digital Object Identifier
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Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AIWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-30Full publication date if available
- Authors
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Reem Alkanhel, Hager Saleh, Ahmed Elaraby, Saleh Alharbi, Hela Elmannai, Saad Alaklabi, Saeed Hamood Alsamhi, Sherif MostafaList of authors in order
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https://doi.org/10.3390/s24237670Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/s24237670Direct OA link when available
- Concepts
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Computer science, Diabetes management, Internet of Things, Artificial intelligence, Diabetes mellitus, Machine learning, Real-time computing, Medicine, Embedded system, Type 2 diabetes, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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