An Intelligent Conversational Healthcare System for Symptom-Based Disease Prediction and Location-Aware Hospital Recommendation Using Machine Learning

Abstract

Abstract : The rapid advancement of artificial intelligence has significantly enhanced the accessibility and efficiency of healthcare services. In today’s fast-paced lifestyle, individuals often neglect early symptoms, leading to delayed diagnosis and severe health complications. To address this challenge, this study proposes an intelligent machine learning-based conversational healthcare assistant capable of analyzing user-reported symptoms and predicting potential diseases with high accuracy. The system integrates Natural Language Processing (NLP), machine learning algorithms, and statistical analysis to enable human-like interaction and effective symptom understanding.The proposed chatbot not only provides disease predictions but also offers meaningful insights into pathology reports and delivers location-aware recommendations for nearby healthcare facilities, thereby improving timely medical intervention. A comprehensive review of recent literature from the past five years has been conducted to identify existing gaps and incorporate advanced methodologies, including deep learning techniques and scalable data processing frameworks. Furthermore, the system is designed with a user-friendly web interface, allowing seamless interaction between users and the chatbot. By combining conversational intelligence with predictive analytics and geospatial awareness, the proposed solution aims to bridge the gap between patients and healthcare services. The study highlights the transformative potential of AI-driven chatbots in enabling early diagnosis, improving healthcare accessibility, and supporting self-assessment in a reliable and efficient manner. IndexTerms - Machine Learning, Conversational Agent, Healthcare Chatbot, Symptom Analysis, Disease Prediction, Machine Learning Artificial intelligence, chatbot, natural language processing, conversational agents,RNN model, geocoder, LSTM , Component,formatting,style,styling,insert.

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