A Comparative Study on Hybrid Deep Learning Models for Depression Detection

Depression is the one of most common mental condition worldwide and it has considerable consequences. Early identification and intervention are key to a successful treatment. The aim of this review is to provide an exhaustive research on the development and status of hybrid deep learning models for depression diagnosis through 2017–2024. We explore various hybrid architectures which combine multiple deep learning techniques, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks and transformer models, often fused with traditional machine-learning algorithms. These data sources are largely qualitative in nature, and include social media, electronic health records, speech tendencies (sarcasm), facial expressions and physiological signs. In practice, we evaluate the performance of and interpretability of these hybrid models in various data sets and cultural settings. It focused particularly on multimodal methods that combine (several) datatypes to increase the accuracy of (anomaly) detection. Key trends that are highlighted cover the increasing use of attention processes, the incorporation of explainable AI methods and models capable to diagnose early-stage depression. We also look at the ethical implications of these devices including data privacy problems and the potential for real-world, clinical application. This comprehensive review highlights the significant step in hybrid deep learning models on depression detection in seven years. It addresses the current constraints and points to future research desires anchoring on the need of bigger, more holistic datasets as well as more interpretable models needed for transforming conceptual findings into practical tools for psychiatric care.