AI Mental Health Tracker and Analyzer Using Machine Learning Technique

Abstract: Mental health problems are one of the major concerns of the 21st century in the field of healthcare. One of the major reasons behind this problem is lack of awareness among masses. The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study amalgamates insights from diverse research endeavours to present a comprehensive systematic review of machine learning (ML) applications in the mental health domain. In this research paper techniques like Logistic Regression, K-NN, Decision Trees, Random Forest, and Support Vector were compared, with Random Forest proving most accurate at 73.16% prediction accuracy.