Identification and Classification of Depressed Patients Using Machine Learning – A Review

Now a days, the frequency of mental illness particularly depression has alarmingly increased. Inadequate early intervention and support for depression identification have led to the rise in associated disorders like anxiety, bipolar, and sleep disorders, as well as, in extreme situations, self-harm and suicide. It is extremely difficult to identify people with mental health issues and to provide prompt therapies. In this paper, the literature review of various techniques is done to detect depression in the patients using machine learning and we found that the accuracy of existing depression diagnosis techniques, which rely on PHQ scores and patient interviews are inadequate. This paper presents the previous research methods to detect depression with advantages and disadvantages of each. It is observed that the detection using LSTM technique gives better results than other machine learning algorithms.