DAC Stacking: A Deep Learning Ensemble to Classify Anxiety, Depression, and Their Comorbidity From Reddit Texts

Area of Science:

• Computational linguistics
• Mental health informatics
• Machine learning for healthcare

Background:

• Depression is a leading cause of global disability, often co-occurring with anxiety.
• Automatic identification of mental health conditions, particularly depression, is increasingly explored using social media data.
• Fewer automated methods exist for detecting anxiety and its comorbidity with depression.

Purpose of the Study:

• To propose DAC Stacking, a novel solution for the automatic identification of depression, anxiety, and their comorbidity.
• To leverage stacking ensembles and Deep Learning (DL) for enhanced mental health condition detection.
• To analyze the effectiveness of different ensemble topologies and DL architectures.

Main Methods:

• Utilized Reddit data for training and evaluating the DAC Stacking model.
• Employed stacking ensembles with single-label binary classifiers (expert and differentiating).
• Integrated a meta-learner for multi-label decision-making and explored various DL architectures and word embeddings.

Main Results:

• DAC Stacking significantly outperformed baseline methods for depression and anxiety detection, achieving f-measures near 0.79.
• The optimal ensemble topology combined three DL architectures with expert and differentiating base models.
• Achieved a Hamming Loss of 0.29 and an Exact Match Ratio of 0.46 with the best performing model.

Conclusions:

• DAC Stacking demonstrates strong performance in automatically identifying depression, anxiety, and their comorbidity.
• The study provides insights into feature importance for mental health classification using SHAP analysis.
• Highlights the potential of ensemble DL methods for nuanced mental health condition detection from social media.