Mental Health Prediction System using LLM and MernStack

This thesis has presented MindCare, a comprehensive AI-powered mental health detection and support web application that successfully bridges the gap between accessible digital tools and meaningful mental health support. The system integrates a clinically-inspired multi-dimensional survey instrument with state-of-the-art generative AI to deliver a personalized, empathetic, and actionable mental health experience. The key contributions of this project include: (1) a novel multi-dimensional scoring algorithm that maps qualitative survey responses to quantitative mental health metrics across 8-11 dimensions; (2) a prompt engineering methodology that leverages actual user responses for highly personalized AI report generation; (3) an integrated AI chatbot that maintains conversational context for meaningful multi-turn mental health support; and (4) a complete, production-ready full-stack web application demonstrating the practical integration of modern web technologies with large language models. Evaluation results confirm that MindCare achieves high user satisfaction (average 4.5/5.0), fast response times, and meaningful personalization. The system represents a significant step toward making AI-assisted mental health support accessible to students and working professionals who may otherwise lack access to professional mental health services.

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