AI-Enhanced Mental Health Diagnosis: Leveraging Transformers for Early Detection of Depression Tendency in Textual Data

Early detection and treatment of depression depend critically on mental health assessment. Artificial intelligence-based methods have shown potential in assessing linguistic and cognitive patterns to spot those who are at risk of depression. This study tries to identify depression tendencies based on linguistic and cognitive characteristics by utilizing a transformer-based language model and self-attention. The goal is to assess how well the trained model performs at correctly identifying people who are predisposed to depression. A variant of the BERT (Bidirectional Encoder Representations from Transformers) model, trained on a larger corpus and for a longer duration, results in improved performance due to its strong capabilities in understanding natural language in the context of detecting depression using text data. The model can classify new text inputs and identify potential signs of depression by learning various linguistic cues and characteristics associated with depression such as sentiment patterns, language usage, emotional expressions, and topics discussed. The study also evaluates the efficacy of other machine learning classification models and long short-term memory networks in detecting depression tendencies. The proposed model achieves an astounding accuracy of 96.86% and is proven as the most effective model for detecting depressive tendencies. It underlines the usefulness of the proposed model in early detection and intervention programs for depression. This study advances mental health assessment and offers important insights into the recognition of mental health concerns by utilizing deep learning and natural language processing models.