A Review of the Integration of Machine Learning Techniques for the Detection of Depression

Abstract

Depression is a widespread mental health disorder that significantly impacts emotional well-being, behavior, and daily functioning. It is characterized by persistent sadness, loss of interest, cognitive impairment, and reduced productivity, making it a major public health concern. Early identification of depression is critical for preventing severe psychological, social, and economic consequences, including increased suicide risk and long-term disability. Traditional diagnostic approaches rely primarily on clinical interviews and self-report questionnaires, which are often subjective, time-consuming, and dependent on expert interpretation. Recent advances in machine learning (ML) have enabled the development of automated systems capable of detecting depressive symptoms through the analysis of diverse data sources, including textual content, speech signals, facial expressions, physiological measurements, and behavioral patterns. This paper presents a comprehensive review of machine learning techniques applied to depression detection. It examines commonly used data modalities, feature extraction strategies, traditional machine learning models, deep learning architectures, multimodal fusion approaches, and evaluation methodologies. A comparative analysis of existing research studies is provided to highlight performance trends and methodological differences. The review indicates that deep learning models and multimodal frameworks generally achieve superior detection performance; however, ethical, privacy, generalizability, and interpretability challenges remain significant barriers to real-world deployment.