Developing an Individual Glucose Prediction Model Using Recurrent Neural Networkopen access
- Authors
- Kim, Dae-Yeon; Choi, Dong-Sik; Kim, Jaeyun; Chun, Sung Wan; Gil, Hyo-Wook; Cho, Nam-Jun; Kang, Ah Reum; Woo, Jiyoung
- Issue Date
- Nov-2020
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Keywords
- continuous glucose monitoring; diabetic inpatient; glucose prediction model; deep learning
- Citation
- Sensors, v.20, no.22, pp 6460 - 6474
- Pages
- 15
- Journal Title
- Sensors
- Volume
- 20
- Number
- 22
- Start Page
- 6460
- End Page
- 6474
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2347
- DOI
- 10.3390/s20226460
- ISSN
- 1424-8220
1424-3210
- 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.
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- Appears in
Collections - College of Medicine > Department of Internal Medicine > 1. Journal Articles
- SCH Media Labs > Department of Big Data Engineering > 1. Journal Articles
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