Home > Archive > 2022 > Volume 12 Number 6 (Nov. 2022) >
IJMLC 2022 Vol.12(6): 301-305 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2022.12.6.1115

Recurrent Neural Network and Convolutional Network for Diabetes Blood Glucose Prediction

Minkai Sheng

Abstract—The convolutional neural network (CNN) is one of the most widely used neural networks. Although the main battlefield is image recognition, natural language processing, and speech recognition, CNNs in other fields also have a good performance. In image recognition, they outperformed humans in some tests. CNN’s have much fewer connections and parameters and so they are easier to train, while their theoretically best performance is likely to be only slightly worse. A Recurrent Neural Network (RNN) is a type of Neural Network used to process sequence data. Compared with ordinary neural networks, it can handle the data of sequence changes. Long short-term memory (LSTM) is a special RNN, mainly to solve the problem of gradient disappearance and gradient explosion in the training process of Long sequence. Simply put, LSTM performs better in longer sequences than a normal RNN. CNN and RNN models are presented to forecast the future glucose levels of patients with type 1 diabetes. Finally, we obtain the predictions of the testing dataset and evaluate the results by the root mean squared error (RMSE). The mean value of the best RMSE of 7.5545.

Index Terms—Convolutional neural network, recurrent neural network, diabetes.

Minkai Sheng is with the Coventry University, UK (e-mail: markshengcn@gmail.com).


Cite: Minkai Sheng, "Recurrent Neural Network and Convolutional Network for Diabetes Blood Glucose Prediction," International Journal of Machine Learning and Computing vol. 12, no. 6, pp. 301-305, 2022.

Copyright @ 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

Article Metrics in Dimensions