From Diagnosis to Treatment: A Systematic Review of Machine Learning Technologies in Depression Care Management

Throughout the course of time, stress, anxiety, and contemporary living styles have exerted significant mental health (MH) impacts on individuals globally. The global advancement of technology in healthcare digitizes extensive data, allowing for a more precise mapping of numerous aspects of human biology compared to old measurement methods. The use of machine learning, or ML, has been recognized as an effective method for recognizing the vast quantities of information that exist in the medical domain. Machine learning approaches are employed in MH to forecast the likelihood of mental diseases and, consequently, implement prospective result of treatment. This research study enumerates many ML techniques employed for the detection and diagnosis of depression. Algorithms for depression identification based on machine learning are classified into 3 categories: classification, deep learning, and ensemble. A comprehensive approach for diagnosing depression encompasses data collecting, preprocessing, training of a machine learning classifier, classification detection, and performance evaluation. Furthermore, it provides a summary to delineate the aims and constraints of various research projects within the field of depression detection.