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

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.