Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
- 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.
- All Author(s)
- D. Y. Kim
; D. S. Choi
; J. Kim
; S. W. Chun
; H. W. Gil
; N. J. Cho
; A. R. Kang
; J. Woo
- Issued Date
- 2020
- Type
- Article
- Keyword
- continuous glucose monitoring; deep learning; diabetic inpatient; glucose prediction model
- Publisher
- MDPI
- ISSN
- 1424-8220
- Citation Title
- Sensors
- Citation Volume
- 20
- Citation Number
- 22
- Citation Start Page
- 6460
- Citation End Page
- 6460
- Language(ISO)
- eng
- DOI
- 10.3390/s20226460
- URI
- http://schca-ir.schmc.ac.kr/handle/2022.oak/2139
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