A novel approach to predict COVID-19 using support vector machine

An unexpected outbreak of 2019 Coronavirus disease (COVID-19) in Wuhan, China, led to a massive catastrophe across the world. The majority of the COVID-19 patients are getting diagnosed with pneumonia in their early stages. Over 22,00,000 confirmed cases have shown various ranges of symptoms, but th...

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Autores principales: Guhathakurata, Soham, Kundu, Souvik, Chakraborty, Arpita, Banerjee, Jyoti Sekhar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137961/
http://dx.doi.org/10.1016/B978-0-12-824536-1.00014-9
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author Guhathakurata, Soham
Kundu, Souvik
Chakraborty, Arpita
Banerjee, Jyoti Sekhar
author_facet Guhathakurata, Soham
Kundu, Souvik
Chakraborty, Arpita
Banerjee, Jyoti Sekhar
author_sort Guhathakurata, Soham
collection PubMed
description An unexpected outbreak of 2019 Coronavirus disease (COVID-19) in Wuhan, China, led to a massive catastrophe across the world. The majority of the COVID-19 patients are getting diagnosed with pneumonia in their early stages. Over 22,00,000 confirmed cases have shown various ranges of symptoms, but the most predominant set includes fever, cough, and shortness of breath. The predominant set of symptoms, coupled with other critical symptoms, a prediction process has been devised in this paper to check whether a person is infected with COVID-19 or not. Based on the crucial impact of the symptoms, we have applied the support vector machine classifier to classify the patient's condition in no infection, mild infection, and serious infection categories. We have achieved an accuracy of 87% in predicting the cases.
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spelling pubmed-81379612021-05-21 A novel approach to predict COVID-19 using support vector machine Guhathakurata, Soham Kundu, Souvik Chakraborty, Arpita Banerjee, Jyoti Sekhar Data Science for COVID-19 Article An unexpected outbreak of 2019 Coronavirus disease (COVID-19) in Wuhan, China, led to a massive catastrophe across the world. The majority of the COVID-19 patients are getting diagnosed with pneumonia in their early stages. Over 22,00,000 confirmed cases have shown various ranges of symptoms, but the most predominant set includes fever, cough, and shortness of breath. The predominant set of symptoms, coupled with other critical symptoms, a prediction process has been devised in this paper to check whether a person is infected with COVID-19 or not. Based on the crucial impact of the symptoms, we have applied the support vector machine classifier to classify the patient's condition in no infection, mild infection, and serious infection categories. We have achieved an accuracy of 87% in predicting the cases. 2021 2021-05-21 /pmc/articles/PMC8137961/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00014-9 Text en Copyright © 2021 Elsevier Inc. All rights reserved. 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
Guhathakurata, Soham
Kundu, Souvik
Chakraborty, Arpita
Banerjee, Jyoti Sekhar
A novel approach to predict COVID-19 using support vector machine
title A novel approach to predict COVID-19 using support vector machine
title_full A novel approach to predict COVID-19 using support vector machine
title_fullStr A novel approach to predict COVID-19 using support vector machine
title_full_unstemmed A novel approach to predict COVID-19 using support vector machine
title_short A novel approach to predict COVID-19 using support vector machine
title_sort novel approach to predict covid-19 using support vector machine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137961/
http://dx.doi.org/10.1016/B978-0-12-824536-1.00014-9
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