Using Machine Learning Techniques to Predict RT- PCR Results for COVID-19 Patients

Abstract

This paper aims to explore the role of Machine Learning (ML) techniques in combating COVID-19. In this study, different ML techniques and data portioning methods will be used to predict RT-PCR results from ER-admitted patients. The data set contains 199 instances with 81 attributes. 5-Fold Cross Validation, 10-Fold Cross Validation, and 80% Training are the different data portioning methods utilized for this research. Decision tree (J48), Random Forest (RaF), and Rotation Forest (RoF), Multi-Layer Perceptron (MLP), Naïve Bayes (NB), K-Nearest Neighbors (kNN), Logistic Regression (LR), LogitBoost (LB), and Sequential Minimal Optimization (SMO) are the main classifiers we explore in this study. The results of our experiments indicate that Rotation Forest gives a highest accuracy of 90% on the data set.