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An Intelligent Web-Based Mental Health Assessment System Using Machine Learning

S. SHIRLEY., M.C.A., (Ph.D)

(Assistant Professor, Department of Master of Computer Applications)

A. AJAYKUMAR., MCA

Christ College of Engineering and Technology

Moolakulam, Oulgaret Municipality, Puducherry – 605010

ABSTRACT

Mental health disorders such as stress, anxiety, and depression have become increasingly prevalent due to modern lifestyle pressures, academic stress, and social isolation. Early identification of mental health risks is critical to prevent long-term psychological complications and improve overall well-being. This project presents an intelligent web-based Mental Health Detection System that leverages machine learning techniques to analyse user lifestyle, behavioural, and emotional parameters. The system collects structured inputs including sleep patterns, work stress, social support, anxiety levels, depressive episodes, and substance use. A trained Random Forest classifier processes these inputs to classify users into three categories: Healthy, Intermediate (needs emotional and social support), and Likely Mental Health Issue. The application also provides confidence scores, personalized wellness suggestions, reflection questions, and a 7-day improvement plan. Developed using Python, Flask, and Scikit-learn, the proposed system demonstrates how data-driven approaches can support early mental health assessment, promote self-awareness, and encourage timely intervention while maintaining user privacy and accessibility.

KEYWORDS

Mental Health Detection, Machine Learning, Random Forest, Mental Wellness, Flask Web Application, Predictive Analytics