Abstract
Recently, heart sound signals have been used in the detection of the heart valve status and the identification of the heart valve disease. Heart sound data sets represents real life data that contains continuous and a large number of features that could be hardly classified by most of classification techniques. Feature reduction techniques should be applied prior applying data classifier to increase the classification accuracy results. This paper introduces the ability of rough set methodology to successfully classify heart sound diseases without the need applying feature selection. 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), Decision tree (DT), Sequential minimal optimization (SMO).
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Salama, M.A., Hassanien, A.E., Platos, J., Fahmy, A.A., Snasel, V. (2012). Rough Sets-Based Identification of Heart Valve Diseases Using Heart Sounds. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_60
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DOI: https://doi.org/10.1007/978-3-642-28942-2_60
Publisher Name: Springer, Berlin, Heidelberg
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