Online Predictor Using Machine Learning to Predict
Novel Coronavirus and Other Pathogenic Viruses

Jnanendra Prasad Sarkar^1,2,+, Indrajit Saha^3,+,*, Nimisha Ghosh^4,5, Debasree Maity⁶

¹Larsen & Toubro Infotech Ltd., Pune, India
²Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
³Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata, India
⁴Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
⁵Department of Computer Science and Information Technology, Institute of Technical Education and Research,
Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
⁶MCKV Institute of Engineering, Liluah, Howrah, India
*Correspondence should be addressed to team leader : indrajit@nitttrkol.ac.in
⁺These team members contributed equally to this work

ABSTRACT

The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. In this regard, machine learning technique can be used to predict the novel coronavirus by considering its sequence. Thus, we are proposing machine learning based novel coronavirus prediction technique, called COVID-Predictor, where 1000 of sequences of SARS-CoV-1, MERS-CoV, SARS-CoV-2 and other viruses are used to train a Naive Bayes classifier so that it can predict any unknown sequences of these viruses. The model has been validated using 10-fold cross validation in comparison with other machine learning techniques. The results show the superiority of our predictor by achieving average 99.3% accuracy on unseen validation set of viruses. The same pre-trained model has been used to design a web based application where sequences of unknown viruses can be uploaded to predict the novel coronavirus.