A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier - Avci, E. (Pergamon-Elsevier Science Ltd, 2009)
In this study, an intelligent system based on genetic-support vector machines (GSVM) approach is presented for classification of the Doppler signals of the heart valve diseases. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound. GSVM is used in this study for diagnosis of the heart valve diseases. The GSVM selects of most appropriate wavelet filter type for problem, wavelet entropy parameter, the optimal kernel function type, kernel function parameter, and soft margin constant C penalty parameter of support vector machines (SVM) classifier. The performance of the GSVM system proposed in this study is evaluated in 215 samples. The test results show that this GSVM system is effective to detect Doppler heart sounds. The averaged rate of correct classification rate was about 95%. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords: Doppler heart sounds; Genetic algorithm; Support vector machine; Optimum feature extraction; Wavelet decomposition; Wavelet entropy; Wavelet kernel