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 the most of predictive data mining methods. The presence of strongly correlated and redundant information in the set of voice descriptors might be one reason of low prediction accuracy. In this paper different feature selection techniques are evaluated by their ability to raise the prediction accuracy discarding irrelevant and redundant voice descriptors when modeling the dependency between functional changes within a phonatory organ and restricted auditory control. As far as the result of prediction varies for different prediction methods, the applicability of certain feature selection technique is considered with respect of prediction method and evaluated in this paper as a feature selection strategy.