A Survey and Taxonomy on AI and Machine Learning Models for Possible Depression Detection

Medical psychiatric depression is one of the most common mental health disorders worldwide and has become a major public health concern. It affects millions of people and can lead to severe consequences such as reduced quality of life, decreased productivity, and in extreme cases, suicide. Early detection of depression is essential for providing timely intervention and treatment. In recent years, advances in artificial intelligence and machine learning have enabled researchers to develop automated systems that can identify possible signs of depression from various types of data. These technologies have shown great promise in supporting mental health professionals by providing efficient, scalable, and data-driven methods for detecting depressive symptoms. This paper presents a systematic review of the existing methods for depression detection along with their salient contribution.