Heart Sound Feature Reduction Approach for Improving the Heart Valve Diseases Identification

Abstract

Recently, heart sound signals have been used in the detection of the heart valve status and the identification of the heart valve disease. Due to these characteristics, therefore, two feature reduction techniques have been proposed prior applying data classifications in this paper. The first technique is the chi-Square which measures the lack of independence between each heart sound feature and the target class, while the second technique is the deep believe network that uses to generate a new data set of a reduced number of features according the partition of the heart signals. The importance of feature reduction prior applying data classification is not only to improve the classification accuracy and to enhance the training and testing performance, but also it is important to detect which of the stages of heart sound is important for the detection of sick people among normal set of people, and which period important for the classification of heart murmur. Different classification algorithms including naive bayesian tree classifier and sequential minimal optimization was applied on three different data sets of 100 extracted features of the heart sound. The extensive experimental results on the heat sound signals data set demonstrate that the proposed approach outperforms other classifiers and providing the highest classification accuracy with minimized number of features.