Open Access

ARTICLE

Prediction of Suitable Candidates for COVID-19 Vaccination

R. Sujatha¹, B. Venkata Siva Krishna¹, Jyotir Moy Chatterjee², P. Rahul Naidu¹, NZ Jhanjhi^3,*, Challa Charita¹, Eza Nerin Mariya¹, Mohammed Baz⁴

1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
2 Department of IT, Lord Buddha Education Foundation & Scientific Research Group in Egypt (SRGE), Kathmandu, Nepal
3 School of Computer Science and Engineering, SCE, Subang Jaya Taylors University, Malaysia
4 Department of Computer Engineering, College of Computer and Information Technology, Taif University, Taif, 21994, Saudi Arabia

* Corresponding Author: NZ Jhanjhi. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 525-541. https://doi.org/10.32604/iasc.2022.021216

Received 27 June 2021; Accepted 16 August 2021; Issue published 26 October 2021

Abstract

In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. However, not every vaccine will be perfect or will get success for everyone. In the present work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables like their food habits and living conditions. The target group for this work will be the healthcare workers, government bodies & medical research organizations. We analyze the data using machine learning techniques & algorithms and predict the working of COVID-19 vaccines on specific age groups developed by significant vaccine manufacturers, i.e., PFIZER\BIONTECH and MODERNA. Data visualization and analysis interpret the vaccine impact based on the above-said variables. It becomes clear that people belonging to a specific demographic factor can have an option to choose the vaccine accordingly based on the previous history of a particular manufacturer’s vaccine getting succeeded for that demographic factor. The various machine learning algorithms we have used are Logistic Regression, Adaboost, Decision Tree, and Random Forest. We have considered the DIED variable as the target variable as this results in a high life threat. On performance measure, perspective Adaboost is showing appreciable values. The prediction of the type of vaccine to be administered could be derived using this machine learning algorithm. The accuracy we achieved based on the experiment are as follows: Decision Tree Classifier with 97.3%, Logistic Regression with 97.31%, Random Forest with 97.8%, AdaBoost with 98.1%.

---

Keywords

---

Citations