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Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Authors :
Dae-Yeon Kim
Dong-Sik Choi
Jaeyun Kim
Sung Wan Chun
Hyo-Wook Gil
Nam-Jun Cho
Ah Reum Kang
Jiyoung Woo
Source :
Sensors, Vol 20, Iss 22, p 6460 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

Details

Language :
English
ISSN :
14248220
Volume :
20
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.15eae6ab6e684e559856313cc9451902
Document Type :
article
Full Text :
https://doi.org/10.3390/s20226460