MentalHealthAI: Utilizing Deep Learning for Accurate Stress Level Prediction

MentalHealthAI is an advanced deep learning system designed to accurately predict stress levels by integrating various data sources, including user surveys, heart rate metrics, sentiment scores from social media interactions, and demographic information. This innovative approach employs a Fully Connected Neural Network (FCNN) architecture, enabling the model to uncover complex relationships among multiple stress-related factors, such as PHQ-9 and GAD-7 questionnaire scores, heart rate variability, sleep patterns, and calculated sentiment scores. By utilizing Natural Language Processing (NLP) techniques, specifically the VADER model, the system analyzes social media text data to derive sentiment scores, providing insights into users' emotional states. This holistic assessment empowers individuals to understand the interplay between their mental health and daily activities, fostering proactive stress management. The advantages of MentalHealthAI include its capacity to deliver timely and personalized stress assessments, allowing users to make informed decisions about their mental well-being. By combining objective data from wearables with subjective sentiment analysis, the system offers a comprehensive perspective on stress dynamics. Ultimately, MentalHealthAI aims to enhance mental health support, providing valuable insights that encourage healthier lifestyles and improved emotional resilience.