基于递归神经网络模型的血糖预测

Yilin Zhang
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引用次数: 0

摘要

提出了一种用于血糖预测的卷积神经网络结构。本文介绍了四种不同的测量方法,包括葡萄糖,膳食,胰岛素和一天中的时间,简称为G, M, I和T。利用个体过去2小时的历史数据预测未来30分钟内的血糖水平,准确度高。为了验证血糖预测模型的有效性,展示并比较了三种主要方法。更具体地说,与神经网络预测血糖(NNPG)和支持向量回归(SVM)相比,递归神经网络(RNN)是更好的血糖预测模型。评价指标为均方根偏差(RMSE)和平均绝对相对差(MARD)。最佳RMSE的平均值为7.75,大大优于其他两个模型。这一结果显示了RNN在准确预测血糖方面的优越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Glucose Prediction Based on the Recurrent Neural Network Model
An advanced convolutional neural network architecture for forecasting blood glucose is proposed in this paper. Four different measures are introduced in this essay, including Glucose, Meal, Insulin, and Time of the day, which are denoted as G, M, I, and T for short. Past 2-hour historical data of individuals are exploited to predict the future glucose level in 30 minutes with high accuracy. To verify the effectiveness of the blood glucose prediction model, three major methods have been displayed and compared. To be more specific, Recurrent Neural Network (RNN) was the better model for forecasting blood glucose, compared with Neural Network Predictive Glucose (NNPG) and Support Vector Regression (SVM). The metrics of evaluation are Root-Mean-Square deviation (RMSE) and Mean Absolute Relative Difference (MARD). The average of the best RMSE is 7.75, which is largely better than those of the other two models. This result shows the superior performance of RNN in accurate glucose prediction.
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