Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after...

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Autores principales:	Syed Ali Jafar Zaidi, Saad Tariq, Samir Brahim Belhaouari
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 vaccine prediction random forest support vector machine k-nearest neighbor Bibliography. Library science. Information resources Z
Acceso en línea:	https://doaj.org/article/0208cf2bee99493499467d707fc20c41
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spelling	oai:doaj.org-article:0208cf2bee99493499467d707fc20c412021-11-25T17:19:50ZFuture Prediction of COVID-19 Vaccine Trends Using a Voting Classifier10.3390/data61101122306-5729https://doaj.org/article/0208cf2bee99493499467d707fc20c412021-11-01T00:00:00Zhttps://www.mdpi.com/2306-5729/6/11/112https://doaj.org/toc/2306-5729Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study focuses on the future prediction of the effectiveness of the COVID-19 vaccine effectiveness which has been presented as a light in the dark. People bear several reservations, including concerns about the efficacy of the COVID-19 vaccine. Under these presumptions, the COVID-19 vaccine would either lower the risk of developing the malady after injection, or the vaccine would impose side effects, affecting their existing health condition. In this regard, people have publicly expressed their concerns regarding the vaccine. This study intends to estimate what perception the masses will establish about the role of the COVID-19 vaccine in the future. Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. A voting classifier was used at the end of this study to determine the accuracy of all the classifiers. The results prove that the SVM produces the best forecasting results and that artificial neural networks (ANNs) produce the worst prediction toward the individual aptitude to be vaccinated by the COVID-19 vaccine. When using the voting classifier, the proposed system provided an overall accuracy of 89.9% for the random dataset and 45.7% for the date-wise dataset. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19 vaccine.Syed Ali Jafar ZaidiSaad TariqSamir Brahim BelhaouariMDPI AGarticleCOVID-19vaccinepredictionrandom forestsupport vector machinek-nearest neighborBibliography. Library science. Information resourcesZENData, Vol 6, Iss 112, p 112 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 vaccine prediction random forest support vector machine k-nearest neighbor Bibliography. Library science. Information resources Z
spellingShingle	COVID-19 vaccine prediction random forest support vector machine k-nearest neighbor Bibliography. Library science. Information resources Z Syed Ali Jafar Zaidi Saad Tariq Samir Brahim Belhaouari Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
description	Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study focuses on the future prediction of the effectiveness of the COVID-19 vaccine effectiveness which has been presented as a light in the dark. People bear several reservations, including concerns about the efficacy of the COVID-19 vaccine. Under these presumptions, the COVID-19 vaccine would either lower the risk of developing the malady after injection, or the vaccine would impose side effects, affecting their existing health condition. In this regard, people have publicly expressed their concerns regarding the vaccine. This study intends to estimate what perception the masses will establish about the role of the COVID-19 vaccine in the future. Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. A voting classifier was used at the end of this study to determine the accuracy of all the classifiers. The results prove that the SVM produces the best forecasting results and that artificial neural networks (ANNs) produce the worst prediction toward the individual aptitude to be vaccinated by the COVID-19 vaccine. When using the voting classifier, the proposed system provided an overall accuracy of 89.9% for the random dataset and 45.7% for the date-wise dataset. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19 vaccine.
format	article
author	Syed Ali Jafar Zaidi Saad Tariq Samir Brahim Belhaouari
author_facet	Syed Ali Jafar Zaidi Saad Tariq Samir Brahim Belhaouari
author_sort	Syed Ali Jafar Zaidi
title	Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
title_short	Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
title_full	Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
title_fullStr	Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
title_full_unstemmed	Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
title_sort	future prediction of covid-19 vaccine trends using a voting classifier
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0208cf2bee99493499467d707fc20c41
work_keys_str_mv	AT syedalijafarzaidi futurepredictionofcovid19vaccinetrendsusingavotingclassifier AT saadtariq futurepredictionofcovid19vaccinetrendsusingavotingclassifier AT samirbrahimbelhaouari futurepredictionofcovid19vaccinetrendsusingavotingclassifier
_version_	1718412448470401024

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

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