Early Depression Detection Using AI: A Web-Based Psychiatrist-Patient Platform

Abstract

Mental health disorders such as depression require continuous monitoring and timely intervention, yet existing solutions often lack real-time tracking and personalized care. The proposed system is a web-based portal designed to enhance mental health management by bridging the gap between psychiatrists and patients. It provides secure login and personalized dashboards, featuring therapeutic videos on themes like depression and emotional well-being. The system employs machine learning techniques, particularly the Random Forest algorithm, to track patient activity, including video engagement and interaction patterns, for early detection of potential mental health concerns. The Random Forest model is chosen for its high classification accuracy and robustness in predicting behavioral trends. Upon identifying concerning behavioral patterns, the system generates alerts for psychiatrists, facilitating timely intervention. Additionally, the platform fosters improved communication, allowing psychiatrists to monitor patient progress, tailor treatment plans, and provide data-driven recommendations. By integrating AI-driven behavioral tracking, video therapy, and predictive analytics, this system aims to offer a proactive and personalized approach to mental health care.