A Glucose Prediction Model based on Variational Mode Decomposition and Least Squares Support Vector Regression. (October 2019)

• Record Type: Journal Article
• Authors: Wen, S; Li, H R; Han, H H; Yu, X
• Is Part Of: IOP conference series. Volume 646(2019)
• Publication Date: 2019-10
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/646/1/012018
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Online prediction of subcutaneous glucose concentration plays a critical role in glucose management for type 1 diabetes. In this work, a new method combining Variational Mode Decomposition (VMD) and Least Squares Support Vector Regression (LSSVR) is proposed with three main stages to improve the prediction accuracy. Firstly, the time series of blood glucose are decomposed into different frequency series by VMD method. Secondly, the LSSVR model is trained to predict each subsequence. Finally, the predicted sequences are reconstructed to obtain the overall glucose predictions. The experimental results demonstrate the effectiveness and accuracy of the proposed model for short term glucose prediction.