Feature selection is an essential pre-processing method to remove irrelevant and redundant data. This paper presents the development of a model for classifying Heart Statlog. The model consists of two stages. In the first stage, genetic algorithm (GA) is used as random search method with Correlation based feature selection as fitness function for identifying significant features. The second stage a fine tuned classification is done using back propagation neural network using GA identified feature subset elicited in the first stage. Experimental results signify that the feature subset identified by the proposed filter when given as input to Back propagation neural network classifier, leads to enhanced classification accuracy.

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Feature Selection Filter Approach Genetic Algorithm Correlation Based Feature Selection Back Propagation Neural Network.