ABSTRACT
The number of confirmed cases of COVID-19 is increasing exponentially day by day across the world because of its super spreading nature. It was started in China and took a very less time to spread all over the globe. Due to its mortality rate, spreading nature, and unavailability of proper medicine and vaccination, it is declared as a pandemic by the World Health Organization (WHO) in March 2020. In this crisis time of the COVID-19 outbreak, technologists try to smooth the lives by minimizing the infection rate and facilitating in-time quality treatment. In this work, we collected the world data of COVID-19 cases in terms of confirmed, recovery, active, and death and provided visualization. We have also tried to find the patient’s risk level in terms of high, medium, and low by analyzing the patient’s symptoms and previous health histories such as high blood pressure, cardiac disease, diabetes, kidney issues, and others. We applied the C4.5 machine learning (ML) classifier to the considered dataset after preprocessing for risk assessment. The results obtained from the study indicate that the algorithm helps in achieving 75% accuracy. © Springer Nature Switzerland AG 2021.