Design And Implementation Of An Ai-powered Mental Health Chatbot Using Machine Learning, Nlp, And Deep Learning

Abstract

Mental Health Disorders Affect Over One Billion People Globally, Yet Timely Professional Support Remains Limited. This Paper Presents MindCare, An AI-powered Mental Health Chatbot Integrating Natural Language Processing (NLP), Machine Learning (ML), And Deep Learning (DL) For Empathetic, Context-aware Conversational Support. The System Employs A Fine-tuned BERT-based Transformer For Emotion And Intent Detection, A Bidirectional LSTM (BiLSTM) For Sentiment Analysis, And A Retrieval-Augmented Generation (RAG) Framework For Clinically Informed Responses. A Crisis Detection Module (CDM) Triggers Emergency Escalation When Suicidal Ideation Is Detected. Evaluation On Empathetic Dialogues And DAIC-WOZ Datasets Demonstrates 94.7% Emotion Accuracy, 91.3% Intent F1-score, And 87.2% User Satisfaction Across 500 Clinical Trial Participants Over 12 Weeks (p < 0.001).

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