A Machine Learning technique to analyze and detect Corona Virus

Abstract

COVID 19 has expanded repeatedly over the whole world, and the number of infected people has been increasing tremendously. COVID 19 has stormed the world in a blink resulting in millions of deaths with economic downfall around the globe. It has triggered a disastrous paradigm shift for the world. Given the unavoidable circumstances, testing for the virus on a rapid daily basis for million people yields the importance of partaking next steps in virus control. The supply chain of traditional Check-up and report time is exorbitant and has the avenue of exceeding the possibility of misreporting. As a result, we have presented Machine learning-based methods for COVID-19 identification. To improve the COVID 19 prediction algorithm, this study indicates the use of exhaustive profiling, SMOTE (Synthetic Minority Oversampling Technique), a classification model, and a deep learning model. This paper goals to provide Machine learning classifier algorithms and Neural Networks with selected attributes to obtain better accuracy and efficacy with a subsequent comparison with different algorithms.