Glucose prediction for type 1 diabetes using KLMS algorithm

For the patients with type 1 diabetes (T1D), it is very important to keep their blood glucose concentration in the normal level by insulin injections. As the glucose level can be checked consistently by continuous glucose monitoring (CGM) system, it enables estimation of near-future glucose predicti...

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Bibliographic Details
Main Authors: Xiaoyu Sun, Xia Yu, Jianchang Liu, Honghai Wang
Format: Conference Proceeding
Language:English
Subjects:
Blood
Diabetes
glucose prediction
Kernel
kernel filtering algorithm
kernel function selection
Mathematical model
Prediction algorithms
Predictive models
Sugar
type 1 diabetes (T1D)
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Summary:For the patients with type 1 diabetes (T1D), it is very important to keep their blood glucose concentration in the normal level by insulin injections. As the glucose level can be checked consistently by continuous glucose monitoring (CGM) system, it enables estimation of near-future glucose prediction by developing a reliable prediction model. In this paper, a kernel-based adaptive filtering algorithm is applied to build prediction models for glucose prediction. Then, the details for selecting a proper kernel function are also investigated. Finally, the efficiency of the proposed kernel-based forecasting method is evaluated in the short-term blood glucose prediction. The relative results are also analyzed to outline the performance of the proposed KLMS algorithm.
ISSN:2161-2927
DOI:10.23919/ChiCC.2017.8027498