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The primary method for identifying mental disorders automatically hastraditionally involved using binary classifiers. These classifiers are trainedusing behavioral data obtained from an interview setup. In this trainingprocess, data from individuals with the specific disorder under considerationare categorized as the positive class, while data from all other participantsconstitute the negative class. In practice, it is widely recognized thatcertain mental disorders share similar symptoms, causing the collectedbehavioral data to encompass a variety of attributes associated with multipledisorders. Consequently, attributes linked to the targeted mental disordermight also be present within the negative class. This data impurity may lead tosub-optimal training of the classifier for a mental disorder of interest. Inthis study, we investigate this hypothesis in the context of major depressivedisorder (MDD) and post-traumatic stress disorder detection (PTSD). The resultsshow that upon removal of such data impurity, MDD and PTSD detectionperformances are significantly improved.

Analyzing the Effect of Data Impurity on the Detection Performances of Mental Disorders