A COMPREHENSIVE REVIEW OF MACHINE LEARNING MODELS FOR COVID-19 PREDICTION

In recent years, the Coronavirus Disease (COVID-19) has emerged as a global public health emergency, spreading rapidly across countries and placing unprecedented pressure on healthcare systems worldwide. The sudden surge in infected individuals has highlighted significant limitations in traditional diagnostic and screening methods, particularly in terms of testing time, cost, and availability of medical resources. As a result, there is a growing need for intelligent, fast, and cost-effective computational approaches to support early diagnosis and clinical decision-making. Machine learning has gained considerable attention as an effective tool for medical data analysis due to its ability to learn patterns from large and complex datasets. With successful applications in healthcare, biosciences, finance, and security, machine learning techniques have demonstrated strong potential in disease detection and prediction tasks. By analyzing clinical features, laboratory test results, and patient health records, machine learning models can assist in identifying COVID-19 cases at an early stage and reduce dependency on time-consuming diagnostic procedures.