Leveraging NLP for Disease Diagnosis and Symptom Analysis

Rapid advancements in NLP, short for Natural Lan- guage Processing, have paved the way for enhancing healthcare systems, particularly in disease diagnosis and symptom analysis. This research explores the application of NLP techniques to develop an intelligent healthcare chatbot capable of interpreting symptoms and diagnosing diseases. By leveraging large-scale health data and pre-trained language models, the chatbot aims to provide real-time, reliable medical advice to users. The system is designed to extract relevant information from user input, such as symptoms, medical history, and specific queries, and match it with disease patterns using semantic analysis and machine learning. Additionally, the chatbot provides users with contextual information about diseases, including severity, treatment options, and prevention methods. This study emphasizes the importance of developing an interactive, accessible, and scalable solution to support healthcare professionals, improve patient engagement, and aid in early detection of diseases. The findings indicate that NLP-based models can significantly enhance diagnostic accuracy and user experience in healthcare settings. This paper also dis- cusses the challenges in handling medical jargon, ensuring data privacy, and integrating the system into real-world healthcare frameworks