Advancements in AI for Depression Detection: A Survey of Machine Learning and Deep Learning Applications

This survey paper delves into the sophisticated applications of machine learning (ML) and deep learning (DL) techniques in the realm of depression detection. By leveraging a variety of analytical methods, including text analysis, speech analysis, social media monitoring, and image and video analysis, the research highlights the role of advanced algorithms such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), Ensemble Methods, Random Forests, Gradient Boosting Machines (GBM), and Graph Neural Networks (GNN) in identifying depressive symptoms. The study conducts a thorough review of existing literature, showcasing the efficacy of these techniques while pinpointing critical gaps and challenges, such as data quality and integration with clinical practice. This comprehensive examination underscores the necessity for robust, generalizable models capable of real-world application. Ultimately, the survey aims to demonstrate how ML and DL technologies can significantly enhance early detection and intervention strategies in mental health management, paving the way for improved patient outcomes.