Role of Machine Learning in Pattern Evaluation of COVID-19 Pandemic: A Study for Attribute Explorations and Correlations Discovery among Variables

Abstract

The Severe Acute Respiratory Syndrome (SARS-CoV-2) has challenged the highly developed health and care systems in countries around the world. This epidemic has spread its feet across the world in such a way that the medical system of developed countries has also collapsed. Even after consuming all the available medical resources, it has become impossible to save human life from this serious disease. Data of COVID-19 has been produced at a rapid pace from around the world using key traditional diagnostic techniques such as CT Scans, X-Ray scans, to identify the disease and this data proved to help machine learning algorithms prove its role in the field of health care. Assuming the base of this data, it is easy to predict the risk of severity with the help of machine learning algorithms. In this study, we analyse the dataset of the 1000 patients and applied the XG Boost classification algorithm for the evaluation of symptoms of COVID-19. Besides, the data was pre-processed for the better accuracy of the work and to find the correlations between the variable for getting the more prominent factors for the analysis of risk factors for disease.