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A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu
COVID-19 has created many complications in today’s world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238245/ https://www.ncbi.nlm.nih.gov/pubmed/37362286 http://dx.doi.org/10.1007/s00500-023-08590-2 |
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author | Koteeswaran, S. Suganya, R. Surianarayanan, Chellammal Neeba, E. A. Suresh, A. Chelliah, Pethuru Raj Buhari, Seyed M. |
author_facet | Koteeswaran, S. Suganya, R. Surianarayanan, Chellammal Neeba, E. A. Suresh, A. Chelliah, Pethuru Raj Buhari, Seyed M. |
author_sort | Koteeswaran, S. |
collection | PubMed |
description | COVID-19 has created many complications in today’s world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies. |
format | Online Article Text |
id | pubmed-10238245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-102382452023-06-06 A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu Koteeswaran, S. Suganya, R. Surianarayanan, Chellammal Neeba, E. A. Suresh, A. Chelliah, Pethuru Raj Buhari, Seyed M. Soft comput Focus COVID-19 has created many complications in today’s world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies. Springer Berlin Heidelberg 2023-06-03 /pmc/articles/PMC10238245/ /pubmed/37362286 http://dx.doi.org/10.1007/s00500-023-08590-2 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 | Focus Koteeswaran, S. Suganya, R. Surianarayanan, Chellammal Neeba, E. A. Suresh, A. Chelliah, Pethuru Raj Buhari, Seyed M. A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu |
title | A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu |
title_full | A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu |
title_fullStr | A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu |
title_full_unstemmed | A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu |
title_short | A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu |
title_sort | supervised learning approach for the influence of comorbidities in the analysis of covid-19 mortality in tamil nadu |
topic | Focus |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238245/ https://www.ncbi.nlm.nih.gov/pubmed/37362286 http://dx.doi.org/10.1007/s00500-023-08590-2 |
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