MINDVERGE: A HEALTH MONITORING SYSTEM USING SENTIMENT ANALYSIS WITH LLM
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Abstract
In the rapidly evolving digital healthcare ecosystem, individuals often face challenges in monitoring their emotional well-being and accessing timely medical support. Many existing healthcare platforms lack intelligent mechanisms to continuously analyze users’ mental health conditions and provide real-time assistance based on their emotional state. To address this issue, this project proposes the development of an intelligent health monitoring system called MindVerge, which integrates advanced sentiment analysis techniques powered by Large Language Models (LLMs) to enhance user well-being and accessibility to healthcare services.
The proposed system follows a structured and multi-functional approach that combines natural language processing and real-time data integration. It analyzes user-generated text inputs such as chat messages or health logs to detect emotional states including stress, anxiety, positivity, or negativity. The system utilizes NLP techniques such as contextual text analysis and deep learning-based language understanding to accurately interpret user emotions and identify potential mental health risks.
In addition to sentiment analysis, the system incorporates location-based healthcare
support using Google Maps API to help users discover nearby hospitals and medical professionals. A distance calculation mechanism based on the Haversine formula ensures accurate recommendations of the nearest healthcare facilities. The platform also includes an appointment booking system that allows users to schedule consultations directly and receive real-time updates on appointment status.
Furthermore, the system provides secure authentication, data privacy, and role-based access for users and hospital staff. Emotional data and appointment records are safely stored and managed within the database to ensure reliability and confidentiality. By integrating intelligent emotional analysis with real-time healthcare connectivity, MindVerge offers a user-friendly, efficient, and proactive solution for continuous mental health monitoring and support.
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