Healthcare is a rapidly growing industry in both developed and developing countries. The expanse of technology has facilitated the storage and analysis of the diverse data which the healthcare industry generates. Data mining algorithms have been employed in the health care industry for the past few years for diverse kind of decision making and predictive analysis related tasks. Classification algorithms have been widely used for early detection of disease symptoms among patients. However, the selection of a suitable classifier for a particular dataset is an important problem in various healthcare related problems. This paper puts forward an empirical comparison of five important classifiers built using decision trees, bayesian learning, support vector machines and ensemble learning on twelve UCI healthcare datasets. The experimental results are examined from multiple perspectives, namely accuracy, precision, recall and F-measure.