Comparison of feature selection strategies for hearing impairments diagnostics

Diagnostics of hearing impairments is a non-trivial problem for data mining techniques. The state of hearing can be described via a measurement of polymorphic disorders in the voice structure that are secondary to restricted auditory control. The diagnostic voice analysis determines voice descriptors that can be used for marginal estimation of the state of hearing. This problem is hard for most of the predictive data mining methods. The presence of strongly correlated and redundant information in the set of voice descriptors might be one reason for the low prediction accuracy. In this paper, different feature selection techniques are evaluated by their ability to raise the prediction accuracy by discarding irrelevant and redundant voice descriptors when modeling the dependency between functional changes within a phonatory organ and restricted auditory control. As the result of the prediction varies for different prediction methods, the applicability of certain feature selection technique is considered with respect to the prediction method and evaluated as a feature selection strategy.