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Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach
Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Hea...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017389/ https://www.ncbi.nlm.nih.gov/pubmed/36968247 http://dx.doi.org/10.1016/j.engappai.2023.106157 |
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author | Zhou, Luyu Zhao, Chun Liu, Ning Yao, Xingduo Cheng, Zewei |
author_facet | Zhou, Luyu Zhao, Chun Liu, Ning Yao, Xingduo Cheng, Zewei |
author_sort | Zhou, Luyu |
collection | PubMed |
description | Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data. |
format | Online Article Text |
id | pubmed-10017389 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100173892023-03-16 Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach Zhou, Luyu Zhao, Chun Liu, Ning Yao, Xingduo Cheng, Zewei Eng Appl Artif Intell Article Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data. The Author(s). Published by Elsevier Ltd. 2023-06 2023-03-16 /pmc/articles/PMC10017389/ /pubmed/36968247 http://dx.doi.org/10.1016/j.engappai.2023.106157 Text en © 2023 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Zhou, Luyu Zhao, Chun Liu, Ning Yao, Xingduo Cheng, Zewei Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach |
title | Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach |
title_full | Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach |
title_fullStr | Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach |
title_full_unstemmed | Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach |
title_short | Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach |
title_sort | improved lstm-based deep learning model for covid-19 prediction using optimized approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017389/ https://www.ncbi.nlm.nih.gov/pubmed/36968247 http://dx.doi.org/10.1016/j.engappai.2023.106157 |
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