Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data

Abstract

Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have a significant role in predicting of COVID-19. In this study, we performed binary classification (COVID-19 vs. other types of coronavirus) by extracting features from genome sequences. Support vector machines, naive Bayes, K-nearest neighbor, and random forest methods were used for classification. We used viral gene sequences from the 2019 Novel Coronavirus Resource Database. Experimental results presented show that a decision tree method achieved 93% accuracy.