Effective Feature Reduction for Health Care Data Set using Hybridized Feature Selector Sundaravadivel P.*, Dr. Kannan S. Senthamarai**, Dr. Balamurugan S. Appavu Alias*** *Department of Computer Science and Engineering, SBM College of Engineering & Technology, Dindugul, India **Department of Computer Science and Engineering, Kalasalingam University, Srivilliputhur, India ***Department of Information Technology, K.L.N. College of Information Technology, Madurai, India Online published on 3 May, 2016. Abstract In this study, diagnosis of Medical disease, which is a very common and important, was conducted with a machine learning system. The proposed medial disease diagnosis approach has three stages. The first stage, the feature number of Medical disease dataset was reduced by feature extraction phase, then further reduced by the feature selection (FS) algorithm in comparison with various ranking based Filter methods and its performance is evaluated by means of five different classifiers. In this study, Feature extraction based pre-processing with Principal Component Analysis is done as initial step, which can improved by ours hybrid method PCA-CFS, is a new method and firstly, it is applied to six medical disease dataset. The dataset used in our study was taken from the UCI machine learning database. The predicted classification accuracy of our system was closer to 90% and it was very promising with regard to the other classification applications in the literature for this problem. Top Keywords Classification accuracy, Dimensionality reduction, Medical disease diagnosis, Feature Extraction, Feature selection. Top |