An expert system based on least square support vector machines for diagnosis of the valvular heart disease

There has been a growing research interest in the use of intelligent methods in biomedical studies. This is the result of developments in the area of data analysis and classifying techniques. In this paper, an expert system based on least squares support vector machines (LS-SVM) for diagnosis of valvular heart disease (VHD) is presented. Wavelet packet decomposition (WPD) and fast-Fourier transform (FFT) methods are used for feature extracting from Doppler signals. LS-SVM is used in the classification stage. Threefold cross-validation method is used to evaluate the proposed expert system performance. The performances of the developed systems were evaluated in 105 samples that contain 39 normal and 66 abnormal subjects for mitral valve disease. The results showed that this system is effective to detect Doppler heart sounds. The average correct classification rate was about 96.13% for normal subjects and abnormal subjects.