Tackling Depression Detection with deep learning a Hybrid Model

This research addresses the pressing issue of insufficient resources for detecting and treating depression, a leading cause of various mental health disorders. Traditional methods, like PHQ scores and interviews, exhibit low accuracy. In response, we propose a novel hybrid model that integrates both textual and audio components of patient responses. The model encompasses diverse architectures, including LSTM, Hybrid LSTM, LSTM+GRU, BiLSTM, Hybrid BiLSTM, and TextCNN, offering a comprehensive approach to depression detection. Leveraging transcript data, our study goes beyond conventional techniques, exploring ensemble methods for enhanced predictive accuracy. Specifically, we introduce the LSTM+GRU ensemble technique, anticipating a remarkable accuracy exceeding 99%. By fusing the strengths of different models, our approach seeks to significantly improve the identification of individuals experiencing mental health challenges, enabling prompt and effective intervention. This research contributes to advancing the field of depression detection, offering a promising solution to address the complexities associated with mental health assessment.