Abstract
COVID-19 has crippled the lives of millions in the world and is continuously doing so without any sight of relief. Even after the roll out of effective vaccines against COVID-19 and more than half of the population inoculated, it is still a widespread concern. This has led to extensive research around the world regarding the prediction of the COVID-19 disease, its diagnosis, developing drugs for its treatment and its forecasting, etc. Machine Learning has proved its significance in almost every domain and its techniques are also being actively used against COVID-19 by the researchers giving satisfactory results. In this paper, we have highlighted some of the efficient research that have been done using machine learning techniques to predict COVID-19 disease and its severity in patients. The performance of those techniques has been discussed and analyzed. We also carried out a comparative analysis of the most common techniques used in terms of accuracy obtained by them. It has been found that Support Vector Machines, Neural Networks and K-Nearest Neighbor models give the best performance in most of the research works.