Classifier Selection for the Prediction of Dominant Transmission Mode of Coronavirus Within Localities: Predicting COVID-19 Transmission Mode

Abstract

The coronavirus disease-2019 (COVID-19) pandemic is an ongoing concern that requires research in all disciplines to tame its spread. Nine classification algorithms were selected for evaluating the most appropriate in predicting the prevalent COVID-19 transmission mode in a geographic area. These include multinomial logistic regression, k-nearest neighbour, support vector machines, linear discriminant analysis, naive Bayes, C5.0, bagged classification and regression trees, random forest, and stochastic gradient boosting. Five COVID-19 datasets were employed for classification. Predictive accuracy was determined using 10-fold cross validation with three repeats. The Friedman's test was conducted, and the outcome showed the performance of each algorithm is significantly different. The stochastic gradient boosting yielded the highest predictive accuracy, 81%. This finding should be valuable to health informaticians, health analysts, and others regarding which machine learning tool to adopt in the efforts to detect dominant transmission mode of the virus within localities. © This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License