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Analysis of COVID-19 Death Cases Using Machine Learning

COVID-19 has threatened the existence of human life for more than the last 2 years. More than 460 million confirmed cases and 6 million deaths have been reported worldwide due to COVID-19. To measure the severity of the COVID-19, the mortality rate plays an important role. Understanding the nature o...

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Detalles Bibliográficos
Autores principales: Aslam, Humaira, Biswas, Santanu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191086/
https://www.ncbi.nlm.nih.gov/pubmed/37220559
http://dx.doi.org/10.1007/s42979-023-01835-9
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author Aslam, Humaira
Biswas, Santanu
author_facet Aslam, Humaira
Biswas, Santanu
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description COVID-19 has threatened the existence of human life for more than the last 2 years. More than 460 million confirmed cases and 6 million deaths have been reported worldwide due to COVID-19. To measure the severity of the COVID-19, the mortality rate plays an important role. Understanding the nature of COVID-19 and forecasting the death cases of COVID-19 require more investigation of the real effect for different risk factors. In this work, various regression machine learning models are proposed to extract the relationship between different factors and the death rate of COVID-19. The optimal regression tree algorithm employed in this work estimates the impact of essential causal variables that significantly affect the mortality rates. We have generated a real-time forecast for the death case of COVID-19 using machine learning techniques. The analysis is evaluated with the well-known regression models XGBoost, Random Forest, and SVM on the data sets of the US, India, Italy, and three continents Asia, Europe, and North America. The results show that the models can be used to forecast the death cases for the near future in case of an epidemic like Novel Coronavirus.
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spelling pubmed-101910862023-05-19 Analysis of COVID-19 Death Cases Using Machine Learning Aslam, Humaira Biswas, Santanu SN Comput Sci Original Research COVID-19 has threatened the existence of human life for more than the last 2 years. More than 460 million confirmed cases and 6 million deaths have been reported worldwide due to COVID-19. To measure the severity of the COVID-19, the mortality rate plays an important role. Understanding the nature of COVID-19 and forecasting the death cases of COVID-19 require more investigation of the real effect for different risk factors. In this work, various regression machine learning models are proposed to extract the relationship between different factors and the death rate of COVID-19. The optimal regression tree algorithm employed in this work estimates the impact of essential causal variables that significantly affect the mortality rates. We have generated a real-time forecast for the death case of COVID-19 using machine learning techniques. The analysis is evaluated with the well-known regression models XGBoost, Random Forest, and SVM on the data sets of the US, India, Italy, and three continents Asia, Europe, and North America. The results show that the models can be used to forecast the death cases for the near future in case of an epidemic like Novel Coronavirus. Springer Nature Singapore 2023-05-17 2023 /pmc/articles/PMC10191086/ /pubmed/37220559 http://dx.doi.org/10.1007/s42979-023-01835-9 Text en © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Aslam, Humaira
Biswas, Santanu
Analysis of COVID-19 Death Cases Using Machine Learning
title Analysis of COVID-19 Death Cases Using Machine Learning
title_full Analysis of COVID-19 Death Cases Using Machine Learning
title_fullStr Analysis of COVID-19 Death Cases Using Machine Learning
title_full_unstemmed Analysis of COVID-19 Death Cases Using Machine Learning
title_short Analysis of COVID-19 Death Cases Using Machine Learning
title_sort analysis of covid-19 death cases using machine learning
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191086/
https://www.ncbi.nlm.nih.gov/pubmed/37220559
http://dx.doi.org/10.1007/s42979-023-01835-9
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