Abstract: Real-world medical data is often heterogeneous containing many cases and features, which prerequisite different processing for different cases. Generally, this means that the subsets of relevant features are different for various cases. The voice descriptors set in the problem of hearing impairments diagnosis is an example of such a heterogeneous domain. Ensemble feature selection techniques are adopted to take into account heterogeneity in data. The goal of this paper is to analyze applicability of approaches to feature selection in diagnostics of hearing impairments in the context of an ensemble classification. Ensemble feature selection produces multiple classifiers for this domain based on feature subsets derived by different feature selection approaches. Especially, we are interested in performing feature selection for each particular case, and take into consideration some hidden heterogeneity in data. We use real world clinical hearing impairment data and compare ensemble classification with single classifier technique.