AI-Based Medical Chatbot for Disease Prediction

AI-Based Medical Chatbot for Disease Prediction

With the rapid growth of artificial intelligence in healthcare, patients increasingly seek instant, accessible medical guidance before visiting a doctor. This project delivers an intelligent conversational system that interacts with users in natural language, collects symptoms through multi-turn dialogue, and predicts the most probable disease using machine learning algorithms. The chatbot is built using Natural Language Processing (NLP) techniques such as tokenization, stemming, and intent classification, combined with ML models including Decision Trees, Naive Bayes, and Support Vector Machines trained on structured symptom-disease datasets. The chatbot features a modern web interface with chat and voice input support, symptom severity scoring, and safe escalation alerts that direct high-risk users to emergency services. Developed in Python using frameworks such as Flask or Streamlit with a trained ML backend, the system reduces unnecessary hospital visits, offers 24/7 health triage, and serves as an excellent AI healthcare chatbot project for final-year students, med-tech startups, and telemedicine platforms seeking automated disease prediction solutions.

Components

Python 3.8+
NLTK (tokenization, stemming)
Scikit-learn
Decision Tree / Naive Bayes / SVM
Flask or Streamlit
Speech recognition library

Key Features

• Natural-language symptom collection through multi-turn dialogue
• NLP pipeline with tokenization, stemming and intent classification
• Decision Tree, Naive Bayes and SVM models trained on symptom-disease datasets
• Chat and voice input support with symptom severity scoring
• Safe escalation alerts that direct high-risk users to emergency services

Applications

Telemedicine platforms, health-tech startups, clinics and final year students building AI chatbot and NLP projects.