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July 10, 2026Open Access

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

Key Points

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.

Abstract

Depression is one of the most prevalent mental health disorders worldwide, making early detection essential for timely intervention. This research explores the use of machine learning and deep learning techniques for detecting depression from textual data. Multiple models, including Logistic Regression, Random Forest, LSTM, and a proposed DistilBERT-BiLSTM-Attention architecture, were evaluated and compared to analyze their effectiveness. The study highlights the potential of Natural Language Processing (NLP) and artificial intelligence in supporting mental health assessment while discussing model performance, challenges, and future research directions.

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