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The aim is to explore the effectiveness of machine learning and deep learning techniques in detecting depression from text data.

July 10, 2026Open Access

AI-Based Depression Detection Using Machine Learning and Deep Learning Techniques

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The aim is to explore the effectiveness of machine learning and deep learning techniques in detecting depression from text data.
Evaluated multiple models including Logistic Regression, Random Forest, LSTM, and a DistilBERT-BiLSTM-Attention architecture.
Compared model performance to determine effectiveness in depression detection.
Machine learning and deep learning models show promise for detecting depression from textual data.
DistilBERT-BiLSTM-Attention architecture outperforms traditional models in accuracy.
Challenges and future research directions for improving model performance were discussed.

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Randomized trial evaluates AI models for depression detection, suggesting benefits for mental health assessment.

Maryam zaheer (2026) studied this question.

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