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Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR)

Predicting glucose levels remains a significant challenge in diabetes management, with various factors influencing regulation. Modern technologies like AI and ML offer potential solutions by implementing prediction systems. This study focuses on utilising the Vector Autoregression (VAR) method to ma...

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Main Authors: Abdul Monir, Nurul Aliss, Mahmud, Farhanahani
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
QA273-280 Probabilities. Mathematical statistics
Online Access:http://eprints.uthm.edu.my/12086/1/P16856_acfde2c4af6aaca13d4e09d0b4c393f7.pdf%204.pdf
http://eprints.uthm.edu.my/12086/
https://doi.org/10.30880/eeee.2024.05.01.034
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spelling my.uthm.eprints.120862024-11-27T08:23:53Z http://eprints.uthm.edu.my/12086/ Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR) Abdul Monir, Nurul Aliss Mahmud, Farhanahani QA273-280 Probabilities. Mathematical statistics Predicting glucose levels remains a significant challenge in diabetes management, with various factors influencing regulation. Modern technologies like AI and ML offer potential solutions by implementing prediction systems. This study focuses on utilising the Vector Autoregression (VAR) method to make accurate predictions, considering factors such as insulin and meal intake. Ten datasets, including blood glucose levels, carbohydrate intake, and insulin intake, were collected using MATLAB Simulink simulations. Python was then used to build predictive models with a 70:30 and 80:20 ratio for training and testing. The VAR model's prediction performance was evaluated using metrics like MAE, RMSE, and MSE. The 80:20 data split with binary insulin values yielded better results for blood glucose prediction, with MAE of 11.25808, RMSE of 12.36846, and MSE of 184.3054. This study offers insights into time series prediction of blood glucose using the VAR machine learning model, potentially enhancing diabetes care. 2024-04-30 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/12086/1/P16856_acfde2c4af6aaca13d4e09d0b4c393f7.pdf%204.pdf Abdul Monir, Nurul Aliss and Mahmud, Farhanahani (2024) Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR). In: EVOLUTION IN ELECTRICAL AND ELECTRONIC ENGINEERING. https://doi.org/10.30880/eeee.2024.05.01.034
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QA273-280 Probabilities. Mathematical statistics
spellingShingle QA273-280 Probabilities. Mathematical statistics
Abdul Monir, Nurul Aliss
Mahmud, Farhanahani
Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR)
description Predicting glucose levels remains a significant challenge in diabetes management, with various factors influencing regulation. Modern technologies like AI and ML offer potential solutions by implementing prediction systems. This study focuses on utilising the Vector Autoregression (VAR) method to make accurate predictions, considering factors such as insulin and meal intake. Ten datasets, including blood glucose levels, carbohydrate intake, and insulin intake, were collected using MATLAB Simulink simulations. Python was then used to build predictive models with a 70:30 and 80:20 ratio for training and testing. The VAR model's prediction performance was evaluated using metrics like MAE, RMSE, and MSE. The 80:20 data split with binary insulin values yielded better results for blood glucose prediction, with MAE of 11.25808, RMSE of 12.36846, and MSE of 184.3054. This study offers insights into time series prediction of blood glucose using the VAR machine learning model, potentially enhancing diabetes care.
format Conference or Workshop Item
author Abdul Monir, Nurul Aliss
Mahmud, Farhanahani
author_facet Abdul Monir, Nurul Aliss
Mahmud, Farhanahani
author_sort Abdul Monir, Nurul Aliss
title Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR)
title_short Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR)
title_full Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR)
title_fullStr Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR)
title_full_unstemmed Multivariate modelling for prediction of time-series blood glucose level using vector autoregression (VAR)
title_sort multivariate modelling for prediction of time-series blood glucose level using vector autoregression (var)
publishDate 2024
url http://eprints.uthm.edu.my/12086/1/P16856_acfde2c4af6aaca13d4e09d0b4c393f7.pdf%204.pdf
http://eprints.uthm.edu.my/12086/
https://doi.org/10.30880/eeee.2024.05.01.034
_version_ 1817845162177462272
score 13.244413

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