Machine Learning Algorithms for Predicting SARS-CoV-2 (COVID-19) – A Comparative Analysis
L. William Mary, S. Albert Antony Raj
Abstract
The new public health crisis threatens the entire world. The infectious virus SARS -CoV-2 (COVID-19) is a new virus that has spread rapidly across the world. Coronavirus spreads faster than the early virus, SARS-CoV and MERS-CoV, the first major beta-coronavirus in the human respiratory system. There are approximately 9,033,508 reported cases worldwide. CT scans have been used to diagnose suspicious negative and positive cases. To detect infected cases and mortality rates, many epidemiological models are being used. Therefore, Machine Learning techniques play a vital role in effective prediction. This technology has been used to extract information on large data sets and predictive performance analysis. As a result, a variety of Machine Learning prediction techniques were employed. This paper aims at determining which Classification method performs a high accuracy rate for the collected data samples of COVID-19 positive cases. The Support Vector Machine (SVM) gives 85% of accuracy, K-Nearest Neighbor gives 80% accuracy, and Naive Bayes (NB) text classifier method gives 65% accuracy.