Cascading GA & CFS for Feature Subset selection in Medical Data Mining

Feature subset selection is of immense importance in the field of data mining. The increased dimensionality of data makes testing and training of general classification method difficult. This paper presents the development of a model for classifying Pima Indian diabetic database (PIDD). The model consists of two stages. In the first stage, genetic algorithm (GA) and Correlation based feature selection have been used in a cascaded fashion. GA rendered global search of attributes with fitness evaluation effected by CFS. The second stage a fine tuned classification is done using artificial neural networks by making the feature subset elicited in the first stage as inputs for the network. Experimental results signify that the feature subset identified by the proposed filter when given as input to Back propagation neural network classifier, lead to enhanced classification accuracy.