An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases

In the last two decades, the use of artificial intelligence methods in biomedical analysis is increasing. This is mainly because of the effectiveness of classification and detection systems have improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the use of linear discriminant analysis (LDA) and adaptive neuro-fuzzy inference system (ANFIS) to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white-denoising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short-time Fourier transform were used. As next step, wavelet entropy was applied to these features. For reducing the complexity of the system, LDA was used for feature reduction. In the classification stage, ANFIS classifier is chosen. To evaluate the performance of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 94% specificity rate was obtained.