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

Authors: Shruti Shelke, Lavanya Gaikwad, Siddhi Unawane, Om Sonawane, Miss. Bharti Ahuja

Country: India

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Abstract: The proposed system is a web portal designed to enhance mental health management by connecting psychiatrists and patients. It offers secure login and personalized dashboards for patients, featuring therapeutic videos on themes like depression and happiness. Using machine learning algorithms, particularly the Random Forest algorithm, the system tracks patient activity, such as video views and interactions, to detect potential mental health concerns like depression. The Random Forest model is employed for its high accuracy and robustness in classification, enabling reliable detection of mental health issues. Alerts are sent to psychiatrists for timely intervention. The platform promotes communication, allowing psychiatrists to monitor progress, customize care, and provide data-driven recommendations. This approach combines video therapy, behavioral tracking, and AI-driven analysis for more proactive, personalized mental health care.

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Paper Id: 232557

Published On: 2025-06-05

Published In: Volume 13, Issue 3, May-June 2025

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