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Artificial intelligence and social networks for early detection of depression
Fidel Cacheda;
Diego Fernandez;
Francisco J. Novoa;
Victor Carneiro
ABSTRACT
Background:
Major Depressive Disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people worldwide. 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 analysis with artificial intelligence.
Methods:
Our best performing method is based on a dual approach that uses a machine learning model to detect depressed subjects and another model to identify non-depressed individuals.
Results:
The results of a thorough evaluation of the method following a time-aware approach that rewards early detections demonstrate that the dual model can improve current state-of-the-art detection models by more than 10%.
Conclusions:
Given the results, we consider that this work can help in the development of new solutions to deal with the early detection of depression on social networks.
Citation
Please cite as:
Cacheda F, Fernandez D, Novoa FJ, Carneiro V
Early Detection of Depression: Social Network Analysis and Random Forest Techniques
