A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier |
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| Authors: | E Avci |
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| Affiliation: | 1. Multimedia Processing Lab, Samsung Advanced Institute of Technology (SAIT), Republic of Korea;2. Institute of Automation, Chinese Academy of Sciences (CASIA), China;3. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea;1. Department of Electronic, Information and Bioengineering, Politecnico di Milano, via Golgi 39, Milan 20133, Italy;2. ForCardioLab, Fondazione per la Ricerca in Cardiochirurgia ONLUS, Milan, Italy;3. Cardiovascular Surgery Department, L. Sacco Hospital, University of Milan, Milan, Italy;1. Institute of Economic Research, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea;2. Department of Statistics, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea;3. Department of Mathematics and Statistics, Lederle Graduate Research Tower, Box 34515, University of Massachusetts Amherst, Amherst, MA, USA;1. Department of Cardiology, Kokura Memorial Hospital, Kitakyushu, Japan;2. Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan;3. Department of Cardiology, Otsu Red Cross Hospital, Otsu, Japan |
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| Abstract: | 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%. |
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