COVID-19 Mortality Prediction using Machine Learning: A Deep Forest Approach

COVID-19 is an extremely deadly disease which has wreaked havoc worldwide. Initially, the first case was reported in the wet markets of Wuhan, China in the early 2020's. Though the mortality rate is low compared to other dangerous diseases, a lot of people have already succumbed to this virus. Vaccines have been successfully rolled out and it seems effective in preventing the severe symptoms of the coronavirus. However, a section of people (the elderly and people with existing comorbidities) still continue to die. It is extremely important to predict the patient vulnerability using machine learning since appropriate medicines and treatments can be given in time and precious lives can be saved. In this research, the deep forest classifier is utilized to predict the COVID-19 casualty status. This classifier requires extremely low hyperparameter tuning and can easily compete with the deep learning classifiers. This algorithm performed better than the traditional machine learning classifiers with an accuracy of 92%. The positive results obtained signifies the potential use of deep forest to prevent unwanted COVID-19 deaths by effectively deploying them in various medical facilities. Further, it can reduce the extreme burden already existing on healthcare systems caused by the novel coronavirus.