Infection with Covid-19 can prompt deadly entanglements. Shockingly, there is little data about how the infection
spreads and how patients are influenced. Information mining is the investigation of enormous datasets to remove covered up and
beforehand obscure examples and connections . In social insurance, information mining procedures have been broadly applied
in various applications including: displaying wellbeing results and anticipating persistent results, assessment of treatment
adequacy, clinic positioning, and disease control. In this research work, we have comprehensively compared different data
classification techniques and their prediction accuracy for Covid-19 disease. wes have compared EJ48, REPTRee, and User
Classifier using performance measures like Accuracy, Error Rate, Consumption Time using WEKA tool. We have also
compared these classifiers on various accuracy measures like TP rate, FP rate.
