EFFECTIVE MEDICAL CONSULTATION SYSTEM USING NATURAL LANGUAGE PROCESSING (NLP)
Abstract
ABSTRACT
Healthcare delivery continues to face critical challenges such as limited access to medical professionals, delayed diagnosis, and ineffective communication between patients and healthcare providers. This project presents the design and development of an effective medical consultation system using Natural Language Processing (NLP), aimed at improving accessibility, accuracy, and efficiency in healthcare services.
The system leverages NLP techniques to interpret patient inputs in natural language, analyze symptoms, and provide intelligent medical suggestions or recommendations. By simulating a conversational interface, the system allows patients to interact seamlessly without requiring technical expertise. It integrates machine learning algorithms and structured medical knowledge to enhance diagnostic support.
The study adopts a system development methodology involving data collection, preprocessing, model training, and system implementation. The results demonstrate that NLP-based consultation systems can significantly reduce response time, improve patient engagement, and assist healthcare professionals in preliminary diagnosis.
This research highlights the transformative potential of NLP in modern healthcare systems, offering scalable solutions that can bridge the gap between patients and quality medical consultation, especially in underserved regions.
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