HIGH ACCURACY DETECTION OF COVID-19 BASED ON NAIVE BAYES CLASSIFIER (NBC)

Didik Wibowo, Michelle Novia, Rarasati Nan Rumaksi, Steffany Indra Gunawan

Abstract


The Coronavirus (COVID-19) outbreak has spread across the globe at such a high speed that the number of infections and deaths of people is increasing swiftly each day. Therefore, it is very important to find positive cases early for medication and control the spread of the disease. Some techniques have been tried to detect COVID-19 in several injuries, but they have limited efficiency. Here, we present application of Naïve Bayes Classifier algorithm to COVID-19 diagnosis. The algorithm only requires two probabilities in order to be executed, which are: prior probability and conditional probability. Experimental results have proven the effectiveness of NBC which could achieve a detection accuracy of more than 90%.


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