This chapter focuses on a study of the performance of a set of ensemble-based classifiers on healthcare data, for example accuracy of classifying a patient as Covid-19 positive or negative based on diverse medical parameters. In healthcare data it is seen that data come for classification may be imbalanced or the considered parameters are heterogeneous or there may be lots of missing values or may have large numbers of attributes. To deals with these issues in this work ensemble based classifier has been considered. Thus, four numbers of ensemble based classifiers (EBC-1, EBC-2, EBC-3 and EBC-4) have been designed. Here, EBC-1 is the basic ensemble based classifier which can works on heterogeneous data. Based on EBC-1, other classifiers have been designed to deals with other issues. Results obtained by all the ensemble-based classifiers on a COVID-19 dataset are compared in terms of classification accuracy and discussed the efficiency of the classifiers on the data.