Rough Set-Based Identification of Heart Valve Diseases Using Heart Sounds

  • Mostafa A. Salama
  • Omar S. Soliman
  • Ilias Maglogiannis
  • Aboul Ella Hassanien
  • Aly A. Fahmy
Part of the Intelligent Systems Reference Library book series (ISRL, volume 43)


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).


Heart sound disease rough set dimensional reduction classification 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mostafa A. Salama
    • 1
  • Omar S. Soliman
    • 2
  • Ilias Maglogiannis
    • 3
  • Aboul Ella Hassanien
    • 2
  • Aly A. Fahmy
    • 2
  1. 1.Department of Computer ScienceBritish University in EgyptCairoEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt
  3. 3.Department of Computer Science and Biomedical InformaticsUniversity of Central GreeceLamiaGreece

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