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LSTM Variants in Blood Glucose Prediction for Diabetic Patients

by Rita Ganguly
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 39
Year of Publication: 2022
Authors: Rita Ganguly
10.5120/ijca2022922489

Rita Ganguly . LSTM Variants in Blood Glucose Prediction for Diabetic Patients. International Journal of Computer Applications. 184, 39 ( Dec 2022), 8-12. DOI=10.5120/ijca2022922489

@article{ 10.5120/ijca2022922489,
author = { Rita Ganguly },
title = { LSTM Variants in Blood Glucose Prediction for Diabetic Patients },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2022 },
volume = { 184 },
number = { 39 },
month = { Dec },
year = { 2022 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number39/32569-2022922489/ },
doi = { 10.5120/ijca2022922489 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:32.856823+05:30
%A Rita Ganguly
%T LSTM Variants in Blood Glucose Prediction for Diabetic Patients
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 39
%P 8-12
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The prediction and management of blood glucose level is indispensable for precautionary of diabetic management. The precious tool like Continuous Glucose Monitoring (CGM) system is used to minutely monitoring and refluxing the blood current glucose level of patients. The result whatever generated by CGM can be very useful prediction of future blood glucose levels with the help of machine learning models. In this research introducing a CGM based approaches on recurrent deep learning which will forecast the future blood glucose levels from the obtained data. In case of deep learning LSTM (Long Short Term Memory) and derived LSTM are used in various complex fields with complex time series data. In this study a survey report is established to determine blood glucose with LSTM based different variants to generate more accuracy in results. The goal of this survey is to lower the differences between predicted CGM values and the finger stick blood glucose readings. The output might indicate that this survey approach is realizable for more appreciable forecasting of BG that enhance and enrich the diabetes management.

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Index Terms

Computer Science
Information Sciences

Keywords

LSTM CGM Deep Learning Blood Glucose RNN

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