A Brief Review of Machine Learning Methods used in Mental Health Research

Machine Learning (ML) is a sub-domain of Artificial Intelligence, and it focuses on the statistical methods to analyse data. Analysis of data can help in understanding the hidden patterns. As the internet is growing, one can expect a plethora of data getting generated. Almost every field, be it medicine, education, businesses across the world, stock exchange, agriculture etc., all are contributing to this data generation. Research is going on unprecedentedly on data collected for useful insights. Mental Health is one of the fields where ML is being used for understanding the patients’ behavior, symptoms, effectiveness of the treatments used and helps the medical practitioners in decision making. The presented study aims to showcase the overview of the machine learning technologies used in health care majorly concerned to mental health and depression along with their pitfalls and future directions. The presented analysis laid a foundation for future work in the domain of mental health and depression analysis using machine learning techniques. The study focuses mainly on analyzing how innovation and health could be inter-related. The challenge is to find such techniques that minimize the incorrect outcomes by the machine learning models and help the medical practitioners to take timely decisions.