RNA Sequence Based Prediction of Coronavirus Using Machine Learning

ABSTRACT

The detection of the virus is a basic concern for the doctors and the virology for over a decades due to the dynamic behavior and mutations of the virus makes it difficult to detect the virus and study its behaviors. Latest computational techniques enables scientists to crate models that are proficient of learning patterns from the data as well as used to make predictions for unseen data.. As machine learning techniques predicts the corona viruses by allowing for their differing genetic purposeful characteristics, we propose machine learning supported coronavirus prediction method Novel-COV-2 Predictor wherever RNA sequences of SARSCoV-1, MERS, and SARS-CoV-2 are used to instruct a classifier so that it can expect any indefinite sequence of these viruses. The RNA sequence is given in the form of the large text files. Consequently, it becomes a text classification complexity. We convert these data in the text files into numerical data using the count vectorization and utilize machine learning to create a model to know the patterns. In this regard, we have considered Support Vector Machine (SVM) algorithm to evaluate and so that SARSCoV-2 can be predicted as untimely as potential to save human life. © 2021 IEEE.