A Prediction of Corona Disease Transmission Using A Traditional Machine Learning Approach

Abstract

In this current scenario, covid pandemic breaks analysis is becoming popular among the researchers. The various data sources from the different countries analyzed to predict the possibility of coronavirus transition from one person to another person. The datasets are not providing more information about the causes of the corona. Many authors provided the solution by using chest X-ray and CT images to predict the corona. In this paper, the covid pandemic transition process from one person to another person was classified using traditional machine learning algorithms. The input labels are encoded and transformed, utilizing the label encoder technique. The XG boost algorithm was outperformed all the other algorithms with overall accuracy and F1-measure of 99%. The Naive Bayes algorithm provides 100% accuracy, precision, recall, and F1-Score due to its improved ability to handle lower datasets.