Classification And Errors Analysis of COVID-19 Using Supervised Machine Learning Algorithm and Visualisation

Abstract

During pandemic many people died as a result of the covid-19 sickness, which appeared in 2019 andspreadovertheworld.TheobjectiveofresearchworkistowardstheoccurrenceofCOVIDtoimproveclassificationaccuracy and threshold curve predictions on real-life dataset for Receiver Operator Characteristics(ROC)value.This paper goals the real-life COVID patients from the five countries to test the experiment. The proposedmethodology involves of two steps; used Weka for calculating the accuracy by applying Decision Table machinelearning classifier and compare the results of all the five countries, secondly, the improvement in ROC value interms of initial care predictions by area under ROC analysis. For our COVID dataset has 209instances and 16attributes, Weka has performed on the number of training instances are 184,number of Rules applied is 20,searchdirection has been applied in forward direction, total number of subsets evaluated is 96,merit of best subset foundis82.609andtimetakentobuildmodelis0.06seconds.Oneadvantageofoursuggestedmodelisthatitkeepstheoriginaldataintact,ensuringexperimentquality.Afurtheradvantageisthatthemodelcanbeused withadditionaldatasetstoproducethehighest accuracyandROC analysisoutcomes