Alkanhel Reem Ibrahim, Saleh Hager, Elaraby Ahmed, Alharbi Saleh, Elmannai Hela, Alaklabi Saad, Alsamhi Saeed Hamood, Mostafa Sherif
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Faculty of Computers and Artificial Intelligence, Hurghada University, Hurghada 84511, Egypt.
Sensors (Basel). 2024 Nov 30;24(23):7670. doi: 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.
对于糖尿病患者而言，控制血糖水平（BGL）是一个重要问题，因为该疾病会影响身体对食物的代谢方式，这使得精确调节胰岛素成为必要。患者必须手动检测血糖水平，这既费力又不准确。许多变量会影响血糖水平变化，使得准确预测具有挑战性。为了提前多步预测血糖水平，我们提出了一种基于门控循环单元（GRU）和卷积神经网络（CNN）的新型混合深度学习模型框架，该框架可集成到支持物联网（IoT）的糖尿病管理系统中，通过在边缘设备上进行实时数据处理来提高预测准确性和及时性。GRU层记录时间关系和序列信息，而CNN层分析传入数据以提取重要特征。使用一个可公开获取的1型糖尿病数据集，将混合模型的性能与独立的长短期记忆（LSTM）、CNN和GRU模型的性能进行比较。研究结果表明，CNN-GRU混合模型的表现优于单一模型，这表明其在基于物联网的糖尿病管理系统中显著改善实时血糖水平预测方面具有潜力。
