A novel approach to predict COVID-19 using support vector machine

An unexpected outbreak of 2019 Coronavirus disease (COVID-19) in Wuhan, China, led to a massive catastrophe across the world. The majority of the COVID-19 patients are getting diagnosed with pneumonia in their early stages. Over 22,00,000 confirmed cases have shown various ranges of symptoms, but the most predominant set includes fever, cough, and shortness of breath. The predominant set of symptoms, coupled with other critical symptoms, a prediction process has been devised in this paper to check whether a person is infected with COVID-19 or not. Based on the crucial impact of the symptoms, we have applied the support vector machine classifier to classify the patient's condition in no infection, mild infection, and serious infection categories. We have achieved an accuracy of 87% in predicting the cases.