Deep Learning for Blood Glucose Prediction: CNN vs LSTM
Deep Learning for Blood Glucose Prediction: CNN vs LSTM
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深度学习血糖预测：CNN 与 LSTM
DOI：
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发表时间：
2020
期刊：
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通讯作者：
A. Idri
中科院分区：
文献类型：
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作者：
T. E. Idrissi;A. Idri
为了管理他们的疾病，糖尿病患者需要通过监测血糖水平（BGL）并预测其未来值来控制血糖水平。这允许通过提前采取建议的行动来避免高或低BGL。在这项研究中，我们提出了一种用于BGL预测的卷积神经网络（CNN）。该CNN与长短期记忆（LSTM）模型进行了比较，包括一步和多步预测。这项工作的目标是：1）确定所提出的CNN的最佳配置，2）使用所获得的CNN确定多步预测（MSF）的最佳策略，预测范围为30分钟，以及3）比较CNN和LSTM模型的一步和多步预测。针对第一个目标，我们进行了一系列的实验，通过参数选择。然后，五个MSF策略的CNN达到第二个目标。最后，对于第三个目标，CNN和LSTM模型之间的比较通过Wilcoxon统计检验进行评估。所有实验均使用10名患者的数据集进行，并通过均方根误差来评估性能。结果表明，所提出的CNN在一步和多步预测方面都明显优于LSTM模型，并且没有MSF策略优于CNN的其他策略。
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 study, we propose a Convolutional Neural Network (CNN) for BGL prediction. This CNN is compared with Long-short-term memory (LSTM) model for both one-step and multi-steps prediction. The objectives of this work are: 1) Determining the best configuration of the proposed CNN, 2) Determining the best strategy of multi-steps forecasting (MSF) using the obtained CNN for a prediction horizon of 30 min, and 3) Comparing the CNN and LSTM models for one-step and multi-steps prediction. Toward the first objective, we conducted series of experiments through parameter selection. Then five MSF strategies are developed for the CNN to reach the second objective. Finally, for the third objective, comparisons between CNN and LSTM models are conducted and assessed by the Wilcoxon statistical test. All the experiments were conducted using 10 patients’ datasets and the performance is evaluated through the Root Mean Square Error. The results show that the proposed CNN outperformed significantly the LSTM model for both one-step and multi-steps prediction and no MSF strategy outperforms the others for CNN.