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

• Published in: Journal of medical Internet research Vol. 21; no. 6; p. e12554
• Main Authors: Cacheda, Fidel, Fernandez, Diego, Novoa, Francisco J, Carneiro, Victor
• Format: Journal Article
• Language: English
• Published: Canada Gunther Eysenbach MD MPH, Associate Professor 10.06.2019; JMIR Publications
• Subjects: Artificial intelligence; Artificial Intelligence - standards; Cognitive style; Depression - psychology; Depressive personality disorders; Female; Humans; Machine learning; Machine Learning - standards; Male; Mental depression; Mental disorders; Original Paper; Remote Sensing Technology - methods; Rewards; Social media; Social network analysis; Social Networking; Social networks; depression; major depressive disorder; machine learning; social media; artificial intelligence
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/12554

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. 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. 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. 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%. 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.