COVID-19: An Efficient Big Data Analytics for SARS-CoV-2 Mutations Prediction: A Machine Learning Approach

Abstract

Objectives: This research article aims to identify the level of further mutations of COVID-19 and its vulnerability. Healthcare workers are urged to take precautionary measures to prevent further damage to their community and to avoid further human deaths. At this stage, it is crucial to study, analyze, and understand the COVID-19 variant mutation. Methods: The mutation of COVID-19, however, has not yet been validated for the way in which it affects the risk of underlying comorbidities. It may be possible to tackle this challenge by using artificial intelligence, machine learning, and deep learning algorithms. We have used the J48 algorithm and the Linear Regression algorithm. These algorithms were then analyzed based on the accuracy they provided after running them in the output window of WEKA data mining. Findings: In this paper, machine learning is used to help clinicians and medical researchers understand COVID-19 variant mutations at various stages. Artificial intelligence-based algorithms provide a better understanding of COVID-19 stages and vulnerability levels. Novelty: Artificial intelligence can be a valuable tool for healthcare operations in this pandemic situation. Moreover, healthcare professionals and a data analysis algorithm will probably arrive at the same conclusion based on the data set. However, the use of machine learning-based techniques described in this article will allow for quicker and earlier diagnosis of any type of pandemic situation in the future.