Classification and Prediction of Covid-19 Using Naive Bayes and Random Forest Algorithm

Abstract

Classification is a supervised learning algorithm in machine learning that involves assigning predefined categories or labels to input data based on their features. The primary objective of classification is to create a model that can precisely predict the appropriate label or class for new, unseen data. Covid-19, an infectious and severe disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), spread from bats to humans through an unknown intermediary in Wuhan, China, in late December 2019. This disease can cause organ damage, affecting vital organs such as the liver, heart,and kidneys, as well asthe blood and the immune system. The focus of this study is to classify and predict Covid-19 using two algorithms: Naïve Bayes (NB) and Random Forest (RF). The research utilizes the COVID_Data.CSV dataset, which comprises a total of 316,800 data points. 70% of the data is used as a training data set, while the rest 30% is allocated for testing. The Naïve Bayes classifier achieves an accuracy of 87.39%, while the Random Forest classifier achieves a slightly higher accuracy of 87.47%. Comparative analysis indicates that the Random Forest classifier outperforms Naïve Bayes and is the superior model and classifier for the classification and prediction of Covid-19 using Machine Learning (ML) technique.