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COVID-19 death risk assessment in Iran using artificial neural network

Since the pandemic spread of COVID-19, it has posed a unique public health concern worldwide due to its increased death rate all around the world. The pandemic disease is caused by the SARS-CoV-2, which is the main cause of Middle East Respiratory Syndrome (MERS) and severe acute respiratory syndrom...

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Main Authors: Ifeoluwapo, R. Adebayo, Supriyanto, Eko, Taheri, Sahar
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
Q Science (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/95934/1/EkoSupriyanto2021_COVID19DeathRiskAssessmentinIran.pdf
http://eprints.utm.my/id/eprint/95934/
http://dx.doi.org/10.1088/1742-6596/1964/6/062117
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spelling my.utm.959342022-07-01T04:14:04Z http://eprints.utm.my/id/eprint/95934/ COVID-19 death risk assessment in Iran using artificial neural network Ifeoluwapo, R. Adebayo Supriyanto, Eko Taheri, Sahar Q Science (General) TA Engineering (General). Civil engineering (General) Since the pandemic spread of COVID-19, it has posed a unique public health concern worldwide due to its increased death rate all around the world. The pandemic disease is caused by the SARS-CoV-2, which is the main cause of Middle East Respiratory Syndrome (MERS) and severe acute respiratory syndrome (SARS). Risk assessment is a vital action toward disease risk reduction as it increases the understanding of the risk factors associated with the disease and allows existing data to decide on adequate preventive and mitigation measures. Machine learning techniques have gained strength since 2000, as it has crucial role in data analysis and is really helpful to develop standard mortality models. This study aims to find the best model for data analysis using the Artificial Neural Network (ANN) and other risk factors, which contribute to the high mortality and morbidity associated with COVID-19 in Iran, to predict the risk of death for the people with different situation. A systematic review and meta-analysis were examined by using patient risk factor data from studies done by researchers to estimate COVID-19 death risk. Risk factors for the disease were extracted from an existing study. Using ANN, the best risk prediction for the disease is calculated. Assessment of a different number of hidden neurons with a different training function using the Bayesian Regularization algorithm, the best training function for the ANN model with 5 hidden neurons is found to have the most satisfying results. The coefficient of determination (R) and Root Mean Square Error (RMSE) was 9.99999e-1 and 4.54201e-19 respectively. 2021-07-23 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95934/1/EkoSupriyanto2021_COVID19DeathRiskAssessmentinIran.pdf Ifeoluwapo, R. Adebayo and Supriyanto, Eko and Taheri, Sahar (2021) COVID-19 death risk assessment in Iran using artificial neural network. In: 1st International Conference on Advances in Computational Science and Engineering, ICACSE 2020, 25 December 2020 - 26 December 2020, Coimbatore, Tamilnadu, Virtual. http://dx.doi.org/10.1088/1742-6596/1964/6/062117
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
TA Engineering (General). Civil engineering (General)
spellingShingle Q Science (General)
TA Engineering (General). Civil engineering (General)
Ifeoluwapo, R. Adebayo
Supriyanto, Eko
Taheri, Sahar
COVID-19 death risk assessment in Iran using artificial neural network
description Since the pandemic spread of COVID-19, it has posed a unique public health concern worldwide due to its increased death rate all around the world. The pandemic disease is caused by the SARS-CoV-2, which is the main cause of Middle East Respiratory Syndrome (MERS) and severe acute respiratory syndrome (SARS). Risk assessment is a vital action toward disease risk reduction as it increases the understanding of the risk factors associated with the disease and allows existing data to decide on adequate preventive and mitigation measures. Machine learning techniques have gained strength since 2000, as it has crucial role in data analysis and is really helpful to develop standard mortality models. This study aims to find the best model for data analysis using the Artificial Neural Network (ANN) and other risk factors, which contribute to the high mortality and morbidity associated with COVID-19 in Iran, to predict the risk of death for the people with different situation. A systematic review and meta-analysis were examined by using patient risk factor data from studies done by researchers to estimate COVID-19 death risk. Risk factors for the disease were extracted from an existing study. Using ANN, the best risk prediction for the disease is calculated. Assessment of a different number of hidden neurons with a different training function using the Bayesian Regularization algorithm, the best training function for the ANN model with 5 hidden neurons is found to have the most satisfying results. The coefficient of determination (R) and Root Mean Square Error (RMSE) was 9.99999e-1 and 4.54201e-19 respectively.
format Conference or Workshop Item
author Ifeoluwapo, R. Adebayo
Supriyanto, Eko
Taheri, Sahar
author_facet Ifeoluwapo, R. Adebayo
Supriyanto, Eko
Taheri, Sahar
author_sort Ifeoluwapo, R. Adebayo
title COVID-19 death risk assessment in Iran using artificial neural network
title_short COVID-19 death risk assessment in Iran using artificial neural network
title_full COVID-19 death risk assessment in Iran using artificial neural network
title_fullStr COVID-19 death risk assessment in Iran using artificial neural network
title_full_unstemmed COVID-19 death risk assessment in Iran using artificial neural network
title_sort covid-19 death risk assessment in iran using artificial neural network
publishDate 2021
url http://eprints.utm.my/id/eprint/95934/1/EkoSupriyanto2021_COVID19DeathRiskAssessmentinIran.pdf
http://eprints.utm.my/id/eprint/95934/
http://dx.doi.org/10.1088/1742-6596/1964/6/062117
_version_ 1738510301522821120
score 13.211869

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