A SURVEY ON MACHINE AND DEEP LEARNING TECHNIQUES USED FOR MENTAL HEALTH DIAGNOSIS AND RISK ESTIMATE

Abstract

Mental health is now starting to use machine learning models to diagnose psychological disorders and make prognostications on the risk factors. These models review intricate clinical, behavioral, and social patterns in data as a means of enhancing early identification, individualized attention, and strategies to cognize risk annotations to mental health. This paper discusses the use of ML in healthcare, particularly as it relates to the area of mental health risk assessment and diagnosis. Clinical and community health management are the contexts in which these ML methods are explained, and they are categorized as supervised, unsupervised, and reinforcement learning. Reviews are carried out of the popular models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression (LR), and Extreme Learning Machine (ELM) on their use in mental health. The paper sheds light on ML-based risk prediction models that use demographic, genetic, and clinical data to highlight high-risk patients so that they can be addressed in advance and help insert personalized care. Also, it looks in the area of severity prediction models of mental health conditions, including depression, via standardized scales, such as PHQ-9 and BDI. The idea of symptom profiling is presented to treat heterogeneity regarding depressive symptoms and advance individualized care. This article identifies the prospects of ML in developing precision mental healthcare.