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

TL;DR

This paper investigates how data impurity, caused by overlapping symptoms among mental disorders, affects the accuracy of classifiers in detecting specific disorders like MDD and PTSD, and demonstrates that removing impurity improves detection performance.

Contribution

It introduces an analysis of data impurity effects on mental disorder detection classifiers and shows that cleaning data enhances detection accuracy for MDD and PTSD.

Findings

Removing data impurity significantly improves detection performance.

Shared symptoms cause data overlap, reducing classifier accuracy.

Data cleaning is crucial for reliable mental disorder classification.

Abstract

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.