Intelligent medical chatbot for early detection of infections

In the rapidly evolving digital landscape, chatbots play a vital role in enhancing human-computer interaction, automating customer support, and improving information accessibility. This paper presents a medical chatbot designed using Natural Language Processing (NLP) and Deep Learning techniques to respond to user queries related to medical information. Developed with the Django framework, the chatbot supports both text and voice interactions. It employs TF-IDF vectorization for query matching and an LSTM-based encoder-decoder model for dynamic response generation. The model is trained on a COVID-19 dataset comprising 88 records across 21 intents. Speech recognition is enabled through the Google Speech API, allowing voice-based communication, while multilingual capabilities are integrated using the Google Translate API. User data and chat history are managed through a MySQL database, and the platform supports user authentication. This web-based application aims to improve access to healthcare information by offering an intelligent, interactive, and user-friendly solution.