Analysis and Classification of COVID-19 Severity Using Machine Learning Techniques

Abstract

The COVID-19 pandemic has placed significant burdens on the human race over the last few years. In such surveillance, higher data analysis is required with advanced predictive modeling. Herein, classification is performed on the outbreak severity using a publicly available dataset of the daily and accumulated case and mortality data concerning COVID-19. After preprocessing, real-time missing and inconsistent values are replaced to enable feature derivation, including rolling averages. Interactive Tableau dashboards are developed to visualize severity classifications, regional trends, and temporal variations, providing dynamic insights into outbreak patterns. Comparative analysis reveals disparities in case distribution across continents, identifying major hotspots. Machine learning models are employed to predict severity levels, achieving strong performance metrics. The KNN model yields the highest accuracy of classification, which is 97.11%, while the Random Forest model is more resistant to noise and enhances the stability of the predictions. These results emphasize the potential role of machine learning and data visualization examples with Tableau for data-driven public health strategies in monitoring and responding to outbreaks.