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An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction
The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-1...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669113/ https://www.ncbi.nlm.nih.gov/pubmed/37999193 http://dx.doi.org/10.3390/biomimetics8070552 |
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author | Tarek, Zahraa Shams, Mahmoud Y. Towfek, S. K. Alkahtani, Hend K. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Eid, Marwa M. Khodadadi, Nima Abualigah, Laith Khafaga, Doaa Sami Elshewey, Ahmed M. |
author_facet | Tarek, Zahraa Shams, Mahmoud Y. Towfek, S. K. Alkahtani, Hend K. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Eid, Marwa M. Khodadadi, Nima Abualigah, Laith Khafaga, Doaa Sami Elshewey, Ahmed M. |
author_sort | Tarek, Zahraa |
collection | PubMed |
description | The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R(2)). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction. |
format | Online Article Text |
id | pubmed-10669113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106691132023-11-17 An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction Tarek, Zahraa Shams, Mahmoud Y. Towfek, S. K. Alkahtani, Hend K. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Eid, Marwa M. Khodadadi, Nima Abualigah, Laith Khafaga, Doaa Sami Elshewey, Ahmed M. Biomimetics (Basel) Article The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R(2)). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction. MDPI 2023-11-17 /pmc/articles/PMC10669113/ /pubmed/37999193 http://dx.doi.org/10.3390/biomimetics8070552 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tarek, Zahraa Shams, Mahmoud Y. Towfek, S. K. Alkahtani, Hend K. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Eid, Marwa M. Khodadadi, Nima Abualigah, Laith Khafaga, Doaa Sami Elshewey, Ahmed M. An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction |
title | An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction |
title_full | An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction |
title_fullStr | An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction |
title_full_unstemmed | An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction |
title_short | An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction |
title_sort | optimized model based on deep learning and gated recurrent unit for covid-19 death prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669113/ https://www.ncbi.nlm.nih.gov/pubmed/37999193 http://dx.doi.org/10.3390/biomimetics8070552 |
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