An AI-Driven Mental Health Chatbot Using Recurrent Neural (RNNs) and Long Short-Term Memory (LSTM)

Abstract

In response to the urgent and widespread concerns surrounding mental health, innovative solutions are essential for effective intervention, support, and care delivery. Mental health conditions require innovative approaches integrating advanced technologies due to their complex nature. While more people are dealing with mental health problems, we haven't fully tapped into using artificial intelligence to build thorough systems for checking, tracking, and aiding them. The existing approaches often lack personalized insights, proactive strategies, and ethical considerations, limiting their effectiveness. Utilizing advanced ML algorithms, including Recurrent Neural Networks (RNNs) and specific Natural Language Processing (NLP) techniques such as sentiment analysis, named entity recognition, and Question Answering Systems, the proposed model analyzes user interactions, sentiment, and data inputs to predict, track, and mitigate a spectrum of mental health challenges. By combining advanced algorithms such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) Networks, and deep learning, along with designs focused on users, ethical considerations, and thorough testing methods, the system aims to revolutionize mental health care delivery, fostering resilience, and promoting well-being across diverse populations. Our preliminary tests focus on evaluating the system's accuracy in predicting mental health challenges. The reported 95% accuracy rate and Root Mean Square Error (RMSE) highlight its effectiveness and reliability. Surpassing traditional methods, these results emphasize the system's value for individuals and healthcare professionals. By prioritizing accuracy, we aim to ensure reliable and personalized mental health support. The AI-driven Mental Health Chatbot represents a groundbreaking approach to mental health care, utilizing advanced AI technologies like machine learning and NLP. Its dynamic adaptation and personalized responses showcase its potential to transform how mental health support is delivered, offering timely assistance based on real-time user interactions.
Keywords:
Mental Health, AI-driven chatbot, Natural Language Processing, Sentiment Analysis, Recurrent Neural Networks, Root Mean Square Error, Long Short-Term Memory, Deep Learning