Personalized Healthcare Assistance: An NLP-Based Chatbot for Symptom Analysis

The growing need for affordable and effective healthcare options has prompted the application of Artificial Intelligence (AI) in medical diagnostics. Personalized Healthcare Assistance, an NLP-powered chatbot that can aid users in initial symptom analysis and offer possible health information, is discussed in this paper. The system uses Google’s Gemini 1.5 Pro, a cutting-edge natural language processing (NLP) model, which is implemented within a Flask-based web application. By employing sophisticated text preprocessing methods like tokenization, stemming, lemmatization, and Part-of-Speech (POS) tagging, the chatbot translates user-entered symptoms and forecasts possible medical conditions with enhanced accuracy. In contrast to conventional symptom checkers, the suggested model employs deep learning-based decision-making and confidence scoring to increase reliability. A comparison with other healthcare chatbots emphasizes the originality of the system regarding precision, contextual perception, and interpretability of responses. Experimental evidence shows the chatbot’s performance in symptom identification and first-line diagnosis suggestions, positioning it as an essential tool for making healthcare more accessible. Future developments can encompass multilingual support, electronic health record (EHR) integration, and artificial intelligence-based individualized health suggestions. This work helps to improve AI-based healthcare systems to fill the gap between users and initial medical understanding using an interactive, scalable, and user-centered platform.