Comparative Analysis of Machine Learning Algorithms for COVID-19 Detection and Prediction

The outbreak of the Covid-19 happened in the year 2020 and the absence of treatment of the virus has motivated researchers to make a thorough study of the virus and its transmission. Researchers belonging to the field of Machine Learning have started implementing various algorithms to be able to identify or anticipate if the virus will be present in the human body. Data Science and Machine Learning are the leading fields of Computer a that would help in the process of detection and prediction of occurrence of events. Our paper is a comparative study of algorithms for COVID-19 analysis. The various algorithms that we have chosen are Logistic Regression, Random Forest, Decision tree and Support Vector Machine. In this study, we developed an interface that would allow the user to select the method they wish to apply to the Covid-19 dataset. As per the requirements of the user, he would be selecting and declaring the most efficient algorithm based on accuracy, precision, F1 score and recall. Based on the symptoms that the user selects on the interface, our model would also be able to forecast whether covid-19 is present in the human body.