The extensive presence of COVID-19 infections has led to global initiatives to regulate and manage the virus, to eradicate it. The use of deep learning (ML) to analyze and combat COVID-19 is one key line of research. This is a hotly debated topic right now. Even though there are numerous surveys in the literature, there is a need to keep up with the fast-increasing number of papers on ML applications connected to COVID-19. This study examines current findings on machine learning methods employed in connection to COVID-19. We consider the potential of machine learning for two primary applications: COVID-19 diagnosis and mortality risk and severity prediction. There are discussions about algorithms kinds, learning data sets, and feature selection. Supervised learning methods make up the majority of the machine learning algorithms employed in these two applications. The developed models have yet to be deployed in real-world applications, and much of the related research is still in its early stages.
Software And Hardware