This project focuses on developing an intelligent healthcare chatbot using Natural Language Processing (NLP) and Machine Learning (ML) to simulate human-like conversations within the healthcare domain. The system interprets user inputs, classifies intents, and provides relevant health-related responses. A structured JSON dataset containing categorized intents, patterns, and responses was used to train the model. The chatbot pipeline includes key NLP processes such as tokenization, lemmatization, and vectorization using Bag-of-Words. The model was trained on labeled data to predict user intents and deliver accurate replies.

Core components of the implementation include:

The chatbot operates as an intelligent conversational assistant built to respond to healthcare-related queries in real time. When a user enters a message — for instance, “I have a sore throat”, the chatbot processes the input through a Natural Language Processing (NLP) pipeline using NLTK. The input is tokenized, lemmatized, and cleaned to remove unnecessary words and symbols, converting it into a format the model can understand. This preprocessed text is then fed into a TensorFlow neural network, trained on an intents.json dataset that maps user intents to relevant healthcare responses. The model classifies the query (for example, symptom inquiry or medical advice) and retrieves an appropriate, pre-defined response that aligns with the user’s need. Every step from text preprocessing to intent detection and response generation happens in real time, creating an experience that feels conversational and human-like. The chatbot continuously improves as new intents are added, enhancing its ability to interpret diverse queries, making it reliable for healthcare information seekers.