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Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm

The rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19...

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Autores principales: Alkhammash, Eman H., Algethami, Haneen, Alshahrani, Reem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684576/
https://www.ncbi.nlm.nih.gov/pubmed/34934420
http://dx.doi.org/10.1155/2021/6089677
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author Alkhammash, Eman H.
Algethami, Haneen
Alshahrani, Reem
author_facet Alkhammash, Eman H.
Algethami, Haneen
Alshahrani, Reem
author_sort Alkhammash, Eman H.
collection PubMed
description The rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19 cases for short-term and medium-term dependencies using adaptive learning. This study aimed to predict daily COVID-19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID-19 prevention measures. A fully connected deep neural network, long short-term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia. The performance metrics for all models were computed, and the results were subjected to comparative analysis to detect the most reliable model. Additionally, statistical hypothesis analysis and correlation analysis were performed on the COVID-19 datasets by including features such as daily mobility, total cases, people fully vaccinated per hundred, weekly hospital admissions per million, intensive care unit patients, and new deaths per million. The results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. The findings of this study contribute to our understanding of COVID-19 containment. This study also provides insights into the prevention of future outbreaks.
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spelling pubmed-86845762021-12-20 Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm Alkhammash, Eman H. Algethami, Haneen Alshahrani, Reem Comput Intell Neurosci Research Article The rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19 cases for short-term and medium-term dependencies using adaptive learning. This study aimed to predict daily COVID-19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID-19 prevention measures. A fully connected deep neural network, long short-term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia. The performance metrics for all models were computed, and the results were subjected to comparative analysis to detect the most reliable model. Additionally, statistical hypothesis analysis and correlation analysis were performed on the COVID-19 datasets by including features such as daily mobility, total cases, people fully vaccinated per hundred, weekly hospital admissions per million, intensive care unit patients, and new deaths per million. The results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. The findings of this study contribute to our understanding of COVID-19 containment. This study also provides insights into the prevention of future outbreaks. Hindawi 2021-12-18 /pmc/articles/PMC8684576/ /pubmed/34934420 http://dx.doi.org/10.1155/2021/6089677 Text en Copyright © 2021 Eman H. Alkhammash et al. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Research Article
Alkhammash, Eman H.
Algethami, Haneen
Alshahrani, Reem
Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm
title Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm
title_full Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm
title_fullStr Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm
title_full_unstemmed Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm
title_short Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm
title_sort novel prediction model for covid-19 in saudi arabia based on an lstm algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684576/
https://www.ncbi.nlm.nih.gov/pubmed/34934420
http://dx.doi.org/10.1155/2021/6089677
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