Authors :
Ashish Zagade; Vedant Killedar; Onkar Mane; Ganesh Nitalikar; Smita Bhosale
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/4pr55hmk
Scribd :
https://tinyurl.com/ysaksj5n
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR804
Abstract :
This research paper presents the development
and implementation of an AI-based medical chatbot for
disease prediction. Leveraging machine learning and
artificial intelligence technologies, the chatbot utilizes
natural language processing (NLP) to understand user
queries and provide accurate information, guidance, and
assistance for various infectious diseases. Motivated by
the global spread of infectious diseases and the need for
accessible healthcare support, the paper outlines the
objectives, algorithm design, dataset description, and
application of the chatbot. The algorithm involves
receiving user input, extracting symptoms, classifying
diseases, and suggesting prevention measures. The
dataset, structured in JSON format, facilitates training
and pattern recognition. The chatbot interface,
accessible across platforms, offers information on
symptoms, prevention measures, hospital bed
availability, and medication options. In conclusion, the
research highlights the potential of AI-based chatbots in
revolutionizing healthcare accessibility and personalized
diagnosis, thereby bridging the gap between users and
healthcare systems.
Keywords :
AI-Based Chatbot, Disease Prediction, Machine Learning, Natural Language Processing (NLP), Healthcare Accessibility, Infectious Diseases, Personalized Diagnosis, Healthcare Support.
This research paper presents the development
and implementation of an AI-based medical chatbot for
disease prediction. Leveraging machine learning and
artificial intelligence technologies, the chatbot utilizes
natural language processing (NLP) to understand user
queries and provide accurate information, guidance, and
assistance for various infectious diseases. Motivated by
the global spread of infectious diseases and the need for
accessible healthcare support, the paper outlines the
objectives, algorithm design, dataset description, and
application of the chatbot. The algorithm involves
receiving user input, extracting symptoms, classifying
diseases, and suggesting prevention measures. The
dataset, structured in JSON format, facilitates training
and pattern recognition. The chatbot interface,
accessible across platforms, offers information on
symptoms, prevention measures, hospital bed
availability, and medication options. In conclusion, the
research highlights the potential of AI-based chatbots in
revolutionizing healthcare accessibility and personalized
diagnosis, thereby bridging the gap between users and
healthcare systems.
Keywords :
AI-Based Chatbot, Disease Prediction, Machine Learning, Natural Language Processing (NLP), Healthcare Accessibility, Infectious Diseases, Personalized Diagnosis, Healthcare Support.
