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🏥 AI-Based Medical Chatbot

🚀 Project Overview

Our AI-powered Medical Chatbot is designed to assist users in identifying possible diseases based on symptoms and providing reliable medical insights. By leveraging Machine Learning the chatbot offers:
✅ Symptom-based disease prediction
✅ Medical information (causes, symptoms, treatment, precautions)
✅ An interactive & user-friendly dashboard

Developed during Google Devs Sprint 2K25, this chatbot is a step toward bridging the gap between patients and healthcare knowledge.

🔧 Tech Stack

  • Frontend: HTML, CSS, JavaScript
  • Backend: Python (Flask)
  • Machine Learning: NLP, XgBoost algorithm, Scikit-learn, oversampling, SMOTE Technique, joblib, Pandas
  • Database: Structured medical dataset (CSV/Excel)

📂 Project Structure

Medical_Chatbot_Dashboard/
│── frontend/             # Frontend UI components  
│── chatbot/              # Chatbot logic & NLP models  
│── models/               # Machine learning models  
│── utils/                # Helper functions & utilities  
│── data/                 # Medical dataset (disease.xlsx)  
│── Report/               # Documentation & reports  
│── app.py                # Main application file  
│── README.md             # Project documentation

🛠️ Setup & Installation

1️⃣ Clone the repository

git clone https://github.com/your-repo/Medical-Chatbot.git
cd Medical-Chatbot

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run the chatbot application

python app.py

Access the chatbot on http://127.0.0.1:5000/

🎯 Features

🔹 Disease Prediction: Enter symptoms to get possible disease suggestions
🔹 Medical Insights: View causes, treatments, and precautions
🔹 User-Friendly Dashboard: Interactive interface for seamless experience

🤖 How It Works

1️⃣ User enters symptoms in the chatbot
2️⃣ NLP processes the input and matches it with the dataset
3️⃣ The chatbot returns possible diseases along with medical insights
4️⃣ Users can get more details or explore other symptoms

👥 Contributors

🎉 Acknowledgments

A huge thanks to Google Cloud & VIT-AP University for organizing the Google Devs Sprint 2K25 Hackathon, where this project was built! 🚀

⭐ Support & Contribution

Like our project? Give us a ⭐ and feel free to contribute! Pull requests are welcome.