Voting Based Classification Method for COVID-19 Prediction

Abstract

COVID-19 dataset comprises date, country, confirmed cases, recovered cases and total deaths. The data is integrated with climate data consisting of humidity, dew, ozone, perception, maximum temperature, minimum temperature, and UV. The artificial intelligence based COVID-19 diagnosis strategies can generate more accurate results, save radiologist time, and make the diagnosis process cheaper and faster than the usual laboratory techniques. The COVID-19 detection has various phases which include pre-processing, feature extraction, classification and performance analysis. In this research work, voting classification method is designed for the COVID-19 prediction. It is analyzed that proposed model increases accuracy, precision and recall for the COVID-19 prediction.