An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases
In this paper, an intelligent diagnosis system based on principle component analysis (PCA) and adaptive network based on fuzzy inference system (ANFIS) for the heart valve disease is introduced. 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 (DHS). Here, the wavelet entropy is used as features. This intelligent system has three phases. In pre-processing phase, the data acquisition and pre-processing for DHS signals are performed. In feature extraction phase, the feature vector is extracted by calculating the 12 wavelet entropy values for per DHS signal and dimension of Doppler signal dataset, which are 12 features, is reduced to 6 features using PCA. In classification phase, these reduced wavelet entropy features are given to inputs ANFIS classifier. The correct diagnosis performance of the PCAANFIS intelligent system is calculated in 215 samples. The classification accuracy of this PCAANFIS intelligent system was 96% for normal subjects and 93.1% for abnormal subjects.
principle component analysis anfis wavelet entropy doppler heart sounds discrete wavelet decomposition