Early Detection of Depression: Social Network Analysis and Random Forest Techniques

Background: Major depressive disorder (MDD) or depression is among
the most prevalent psychiatric disorders, affecting more than 300
million people globally. Early detection is critical for rapid
intervention, which can potentially reduce the escalation of the
disorder. Objective: This study used data from social media
networks to explore various methods of early detection of MDDs
based on machine learning. We performed a thorough analysis of the
dataset to characterize the subjects’ behavior based on different
aspects of their writings: textual spreading, time gap, and time
span. Methods: We proposed 2 different approaches based on machine
learning singleton and dual. The former uses 1 random forest (RF)
classifier with 2 threshold functions, whereas the latter uses 2
independent RF classifiers, one to detect depressed subjects and
another to identify nondepressed individuals. In both cases,
features are defined from textual, semantic, and writing
similarities. Results: The evaluation follows a time-aware approach
that rewards early detections and penalizes late detections. The
results show how a dual model performs significantly better than
the singleton model and is able to improve current state-of-the-art
detection models by more than 10%. Conclusions: Given the results,
we consider that this study can help in the development of new
solutions to deal with the early detection of depression on social
networks.

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Early Detection of Depression: Social Network Analysis and Random Forest Techniques