An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of ill...

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Autores principales:	A Lakshmanarao, M Raja Babu, T Srinivasa Ravi Kiran
Formato:	article
Lenguaje:	EN
Publicado:	International Association of Online Engineering (IAOE) 2021
Materias:	covid19 kaggle machine learning regression Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/98e1c5905a0344cba6a250343129ad6e
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spelling	oai:doaj.org-article:98e1c5905a0344cba6a250343129ad6e2021-11-16T07:23:29ZAn Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms2626-849310.3991/ijoe.v17i11.25209https://doaj.org/article/98e1c5905a0344cba6a250343129ad6e2021-11-01T00:00:00Zhttps://online-journals.org/index.php/i-joe/article/view/25209https://doaj.org/toc/2626-8493<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language</p>A LakshmanaraoM Raja BabuT Srinivasa Ravi KiranInternational Association of Online Engineering (IAOE)articlecovid19kagglemachine learningregressionComputer applications to medicine. Medical informaticsR858-859.7ENInternational Journal of Online and Biomedical Engineering, Vol 17, Iss 11, Pp 176-184 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid19 kaggle machine learning regression Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	covid19 kaggle machine learning regression Computer applications to medicine. Medical informatics R858-859.7 A Lakshmanarao M Raja Babu T Srinivasa Ravi Kiran An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms
description	<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language</p>
format	article
author	A Lakshmanarao M Raja Babu T Srinivasa Ravi Kiran
author_facet	A Lakshmanarao M Raja Babu T Srinivasa Ravi Kiran
author_sort	A Lakshmanarao
title	An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms
title_short	An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms
title_full	An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms
title_fullStr	An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms
title_full_unstemmed	An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms
title_sort	efficient covid19 epidemic analysis and prediction model using machine learning algorithms
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/98e1c5905a0344cba6a250343129ad6e
work_keys_str_mv	AT alakshmanarao anefficientcovid19epidemicanalysisandpredictionmodelusingmachinelearningalgorithms AT mrajababu anefficientcovid19epidemicanalysisandpredictionmodelusingmachinelearningalgorithms AT tsrinivasaravikiran anefficientcovid19epidemicanalysisandpredictionmodelusingmachinelearningalgorithms AT alakshmanarao efficientcovid19epidemicanalysisandpredictionmodelusingmachinelearningalgorithms AT mrajababu efficientcovid19epidemicanalysisandpredictionmodelusingmachinelearningalgorithms AT tsrinivasaravikiran efficientcovid19epidemicanalysisandpredictionmodelusingmachinelearningalgorithms
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An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

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