Short-term prediction for dynamic blood glucose trends based on ARIMA-LSSVM-GRU model

Abstract

Continuous glucose monitoring (CGM) is an effective strategy to dynamically monitor the patient’s blood glucose (BG) levels. The current existing prediction models, such as ARIMA, LSSVM, GRU, LSTM are commonly used for the prediction of BG changes in the process of health monitoring and evaluation. However, these single models cannot obtain the optimal prediction results for the BG series, which possess both linear and nonlinear characteristics. Given the above limitation, a hybrid ARIMA-LSSVM-GRU model based on ARIMA, LSSVM, and GRU technologies is proposed to predict the forthcoming BG trends. This model utilizes an ARIMA model to capture the linear features and predict the BG series. Then, the least-squares support vector machine is used to predict the error series that is generated by ARIMA. Finally, the GRU model is used to combine the prediction results of ARIMA and LSSVM to get the more accurate upcoming BG trends. To test the accuracy of this hybrid model, the BG series is prepared, trained, and tested through real clinical BG monitoring data set. The ARIMA-LSSVM-GRU model is been compared with other prediction models (ARIMA, LSSVM, GRU, LSTM) by the evaluation criteria RMSE, MAPE, TIC. The experimental results show that this model can effectively improve the accuracy of the short-term BG prediction.