Published Paper Details:

Hospitals are the most widely used means by which a sick person gets medical check-ups, disease diagnosis and treatment recommendation. This has been a practice by almost all the people over the world. People consider it as the most reliable means to check their health status. The proposed system is to create an alternative to this conventional method of visiting a hospital and making an appointment with a doctor to get diagnosis. This research intends to apply the concepts of natural language processing and machine learning to create a chatbot application. People can interact with the chatbot just like they do with another human and through a series of queries, chatbot will identify the symptoms of the user and thereby, predicts the disease and recommends treatment. This system can be of great use to people in conducting daily check-ups, makes people aware of their health status and encourages people to make proper measures to remain healthy. The ability to accurately predict and diagnose diseases based on patient-reported symptoms is a longstanding challenge in healthcare. Recent advancements in natural language processing (NLP) present new opportunities to leverage the rich clinical information contained in unstructured patient communications. In this study, we developed and validated the Health Guardian platform, an NLP-powered disease prediction system that analyzes free-text patient descriptions to provide real-time risk assessments and clinical decision support. Our results demonstrate the potential of this approach to enhance early detection, optimize treatment pathways, and improve health outcomes. Our research demonstrates the system's ability to identify patterns and correlations in medical narratives, potentially revolutionizing preventive care and clinical decision-making.