Healthy QA Chatbot

Problem

Providing professional, accurate, and detailed answers to users' health-related questions is difficult, and the project investigates how a chatbot intelligent agent can understand user needs and assist in healthcare environments.

Approach

• —Built a medical Q&A chatbot in Python trained on a comprehensive dataset of medical questions and answers.
• —Compared classical machine learning models — neural networks, random forests, support vector machines (SVM), and multilayer perceptrons (MLP).
• —Explored advanced conversational approaches including Seq2Seq, transformer-based, retrieval-based, and reinforcement-learning-based models.
• —Analyzed and synthesized model results to assess effectiveness for professional, accurate healthcare responses and future integration.

Architecture

• —Python implementation (code in a py/ directory plus a packaged project archive).
• —Multiple modeling strategies evaluated side by side: NN, random forest, SVM, MLP, Seq2Seq, transformer-based, retrieval-based, and RL-based models.
• —Models trained on a medical question-and-answer dataset.

Impact

• —The study concludes that chatbot development can effectively understand user needs and provide professional, accurate, and detailed responses, supporting the use of healthcare QA chatbots in medical environments.

Stack