| dc.description.abstract | Taking into account that heart auscultation remains the dominant method for heart examination in the small health centers of the rural areas and generally in primary healthcare set-ups, the enhancement of this technique would aid significantly in the diagnosis of heart diseases. In this context, this chapter introduces the ability of rough set methodology to successfully classify heart sound diseases without the need applying feature selection. Heart sound data sets represents real life data that contains continuous attributes and a large number of features that could be hardly classified by most of classification techniques. Discretizing the raw heart sound data and applying a feature reduction approach should be applied prior any classifier to increase the classification accuracy results. The capabilities of rough set in discrimination, feature reduction classification have proved their superior in classification of objects with very excellent accuracy results. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including Support Vector Machine (SVM), Hidden Naive Bayesian network (HNB), Bayesian network (BN), Naive Bayesian tree (NBT) [9], Decision tree (DT), Sequential minimal optimization (SMO). © Springer-Verlag Berlin Heidelberg 2013. | en |