Voice disorders identification based on different feature reduction methodologies and support vector machine

Identification of voice disorders has been a vital role in our life nowadays. Acoustic analysis can be useful tool to diagnose voice disorders as a complementary technique to other medicine methods such as Laryngoscopy and Stroboscopy. In this paper, we scrutinized feature reduction techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) as feature subset extraction methods and individual feature selection (IFS), forward feature selection (FFS), backward feature selection (BFS) and branch and bound feature selection (BBFS) as feature subset selection procedures. Performance of each method is evaluated by different classifiers. Between feature selection methods, individual feature selection followed by SVM classifier shows the best recognition rate of 91.55% and AUC of 95.80% among these methods. The experimental results demonstrated that highest performance could be achieved by recognition rate of 94.26% and AUC of 97.94% using linear discriminant analysis along with support vector machine as a classifier. Also this mixture has lowest order of computational complexity in comparison with other architectures.