COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms

Nasiba M. Abdulkareem; Adnan Mohsin Abdulazeez; Diyar Qader Zeebaree; Dathar A. Hasan

doi:10.48161/qaj.v1n2a53

Authors

Nasiba M. Abdulkareem Duhok Polytechnic University Duhok, Iraq
Adnan Mohsin Abdulazeez President of Duhok Polytechnic University Duhok, Iraq
Diyar Qader Zeebaree Duhok Polytechnic University Duhok, Iraq
Dathar A. Hasan Duhok Polytechnic University Duhok, Iraq

DOI:

https://doi.org/10.48161/qaj.v1n2a53

Keywords:

COVID-19 Vaccine, Machine learning, Classification algorithm, Dataset, weka

Abstract

In December 2019, SARS-CoV-2 caused coronavirus disease (COVID-19) distributed to all countries, infecting thousands of people and causing deaths. COVID-19 induces mild sickness in most cases, although it may render some people very ill. Therefore, vaccines are in various phases of clinical progress, and some of them being approved for national use. The current state reveals that there is a critical need for a quick and timely solution to the Covid-19 vaccine development. Non-clinical methods such as data mining and machine learning techniques may help do this. This study will focus on the COVID-19 World Vaccination Progress using Machine learning classification Algorithms. The findings of the paper show which algorithm is better for a given dataset. Weka is used to run tests on real-world data, and four output classification algorithms (Decision Tree, K-nearest neighbors, Random Tree, and Naive Bayes) are used to analyze and draw conclusions. The comparison is based on accuracy and performance period, and it was discovered that the Decision Tree outperforms other algorithms in terms of time and accuracy.

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