A comparative study on machine learning algorithms for improved prediction measures for COVID-19

Abstract

The Corona-virus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Corona virus causes respiratory ailment like influenza with symptoms for example, cold, coughs, fatigue, fever and gradually increases the breathing problem. The disease and symptoms are changing frequently thus due to time constraints it is literally impossible to test. Analysis of Covid-19 data using machine learning paradigm is becoming a major interest of the researchers in this situation. The aim of this study is to develop a better predicting model for Covid-19 patients. Patients feature can be assessed statistically and traditionally. But with this day and age of advanced machine learning approaches Covid-19 can be predicted using machine learning techniques with better accuracy. In this work four well known machine learning approaches was used for better prediction in Covid-19. However, this study focuses on optimizing machine learning approaches. Two optimization approaches employed for Grid Search and Random Search are used for fine tune in prediction.