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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.

Abstract-For evolving trustworthy software, engrossing on uncovering process of fault in software is central. Nevertheless, during testing, modifications in the testing routine, defect gravity or testing-skill maturity and working environment, there can be notable change in fault detection rate. When this sort of pattern is observed in testing time it is called change point. In this article, we inquire a resource distribution problem that optimally distributes software developing resources in such a way that the cost of development is curtailed to optimization. In this problem, for all modules the effect of chief circumstantial element of change-point is considered. The constraint of pulling off the desired reliability level for every individual module is also incorporated in the formulation of the problem. A framework based on Karush Kuhn Tucker (KKT) conditions is presented to work out the resulting non-linear optimization problem. A simulated numerical illustration has been analyzed to reflect the formulation of the case and its solution by the algorithm proposed.