Survey on Depression Detection Using Machine Learning Techniques⋆

The widespread issue of depression has provoked the adoption of machine learning techniques for its detection and diagnosis. This survey paper presents a survey of 40 journal papers from 2018 to 2023, focusing on applying machine learning techniques for depression detection. The analysis explores the methodologies, outcomes, and datasets employed in these studies, thereby providing insight into how this field has changed over the time above. The analysis covers a variety of machine learning algorithms, from the latest deep learning models to traditional classifiers. It provides a critical assessment of their effectiveness in differentiating between people who are depressed and those who are not. It also clarifies the variety of data sources used, such as medical records, questionnaires and social media platforms, which advances the field of depression diagnosis. Compiling these 40 journal papers offers insightful information about depression identification with machine learning approaches today, highlighting possible directions for further research in this crucial area of mental health.