Comparative Analysis of Supervised Machine Learning Algorithms for COVID-19 Prediction

Abstract

With the emergence of COVID-19 as an unprecedented pandemic, the health structure of both the developed and underdeveloped world not only seemed stranded but terrible. The human interface was faced with the dilemma of infection causing the health workers fall prey to the disease while identifying the presence of the disease among the patients. Given the nature of the disease, it is needed to mitigate the effects of spread by resorting to technological advancements for diagnosis of the disorder using machine learning algorithms. In this paper, three supervised machine learning algorithms; Decision Tree, Naïve Bayes, and Logistic Regression have been utilized for the prediction of the disease encompassing nine attributes considering various combinations of symptoms. A comparative analysis of the algorithms used revealed that Decision Trees with 99% accuracy and 98% precision, rendered it the most viable and accurate technique for the diagnosis of COVID-19 disease.