Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
<div><p>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 diff...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2021
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513506057912320 |
|---|---|
| author | Syed Ali Jafar Zaidi (19563178) |
| author2 | Saad Tariq (19563181) Samir Brahim Belhaouari (9427347) |
| author2_role | author author |
| author_facet | Syed Ali Jafar Zaidi (19563178) Saad Tariq (19563181) Samir Brahim Belhaouari (9427347) |
| author_role | author |
| dc.creator.none.fl_str_mv | Syed Ali Jafar Zaidi (19563178) Saad Tariq (19563181) Samir Brahim Belhaouari (9427347) |
| dc.date.none.fl_str_mv | 2021-11-02T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/data6110112 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Future_Prediction_of_COVID-19_Vaccine_Trends_Using_a_Voting_Classifier/26968450 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Machine learning COVID-19 vaccine prediction random forest support vector machine k-nearest neighbor decision tree artificial neural network |
| dc.title.none.fl_str_mv | Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>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.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Data<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/data6110112" target="_blank">https://dx.doi.org/10.3390/data6110112</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_30f05a6e621ca3784ef18ee48d3a49fd |
| identifier_str_mv | 10.3390/data6110112 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26968450 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Future Prediction of COVID-19 Vaccine Trends Using a Voting ClassifierSyed Ali Jafar Zaidi (19563178)Saad Tariq (19563181)Samir Brahim Belhaouari (9427347)Information and computing sciencesMachine learningCOVID-19vaccinepredictionrandom forestsupport vector machinek-nearest neighbordecision treeartificial neural network<div><p>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.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Data<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/data6110112" target="_blank">https://dx.doi.org/10.3390/data6110112</a></p>2021-11-02T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/data6110112https://figshare.com/articles/journal_contribution/Future_Prediction_of_COVID-19_Vaccine_Trends_Using_a_Voting_Classifier/26968450CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269684502021-11-02T03:00:00Z |
| spellingShingle | Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier Syed Ali Jafar Zaidi (19563178) Information and computing sciences Machine learning COVID-19 vaccine prediction random forest support vector machine k-nearest neighbor decision tree artificial neural network |
| status_str | publishedVersion |
| title | 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_short | Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier |
| title_sort | Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier |
| topic | Information and computing sciences Machine learning COVID-19 vaccine prediction random forest support vector machine k-nearest neighbor decision tree artificial neural network |