  The primary method for identifying mental disorders automatically has
traditionally involved using binary classifiers. These classifiers are trained
using behavioral data obtained from an interview setup. In this training
process, data from individuals with the specific disorder under consideration
are categorized as the positive class, while data from all other participants
constitute the negative class. In practice, it is widely recognized that
certain mental disorders share similar symptoms, causing the collected
behavioral data to encompass a variety of attributes associated with multiple
disorders. Consequently, attributes linked to the targeted mental disorder
might also be present within the negative class. This data impurity may lead to
sub-optimal training of the classifier for a mental disorder of interest. In
this study, we investigate this hypothesis in the context of major depressive
disorder (MDD) and post-traumatic stress disorder detection (PTSD). The results
show that upon removal of such data impurity, MDD and PTSD detection
performances are significantly improved.
