A Web Application Based Mental Health & Illness Diagnosis with Machine Learning Approach and NLP Based Chat System

This research aims to transform mental health care by addressing critical gaps in existing systems and making support universally accessible free of cost. A centralized platform with a dynamic website has been developed, integrating advanced technologies for mental health illness early detection and an empathetic chatbot that responds personalized to users' emotional needs such as identifying issues and showing guidance, ensuring timely interventions The platform is designed to support individuals facing mental health challenges who may lack access to traditional therapy due to financial or other constraints, combining early detection models for mental health disorders with a compassionate chatbot that offers guidance and emotional support.. The outcome achieves high reliability and effectiveness, with detection models reaching prominent accuracy rates: 97% for depression detection (AdaBoost), 92% for suicidal tendencies (Stochastic Gradient method), and 89% for stress (Naive Bayes). NLTK is used to process the chats through a series of steps like tokenization and following entity classifiers for a better understanding of the chats and to give efficient responses to users' queries. This research Integrates vital mental health resources into a free, accessible platform, addressing a substantial gap in mental health care today. Additionally, the platform offers a curated list of certified mental health professionals and care homes for those seeking professional support when it's needed.