🧠 Multiple Disease Prediction Web App

A multi-disease prediction system built using Streamlit and Machine Learning models to detect:

• 🩸 Diabetes
• ❤️ Heart Disease
• 🧠 Parkinson's Disease

🔗 Live App: https://multiple-disease-prediction-test-therealvinayak.streamlit.app/

🚀 Features

• 🧪 Predict the likelihood of three major diseases based on health parameters
• 💡 Powered by trained ML classification models
• 📊 Clean, interactive Streamlit UI
• 📱 Mobile-friendly and responsive layout
• ⚡ Real-time predictions with instant results
• 🎯 High accuracy models trained on validated datasets

📦 Tech Stack

• Frontend/UI: Streamlit
• Backend: Scikit-learn, Pandas, NumPy
• Deployment: Streamlit Cloud
• ML Models: Logistic Regression, SVM, Random Forest (per disease)
• Data Processing: Pandas, NumPy
• Model Serialization: Pickle

🖥️ App Screenshots

🛠️ Installation (Local)

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup Instructions

1. Clone the repository

git clone https://github.com/therealvinayak/multiple-disease-prediction.git
cd multiple-disease-prediction

2. Create virtual environment (recommended)

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the app

streamlit run app.py

5. Access the app
   • Open your browser and go to http://localhost:8501

📝 Usage

1. Select Disease Type: Choose from Diabetes, Heart Disease, or Parkinson's Disease using the sidebar
2. Input Health Parameters: Enter the required health metrics and symptoms
3. Get Prediction: Click the predict button to get instant results
4. View Results: See the prediction probability and risk assessment

Input Parameters by Disease:

Diabetes Prediction:

• Glucose level, Blood pressure, BMI, Age, etc.

Heart Disease Prediction:

• Chest pain type, Cholesterol, Maximum heart rate, etc.

Parkinson's Disease Prediction:

• Voice measurements, Tremor indicators, Motor symptoms, etc.

⚙️ Deployment

Streamlit Cloud Deployment

1. Push to GitHub: Ensure your code is pushed to a GitHub repository
2. Connect to Streamlit Cloud:
   • Go to streamlit.io/cloud
   • Connect your GitHub account
   • Select your repository
3. Configure Deployment:
   • Set app.py as the main entry point
   • Add any required secrets/environment variables
4. Deploy: Click deploy and your app will be live!

Local Deployment with Docker (Optional)

# Build Docker image
docker build -t disease-prediction-app .

# Run container
docker run -p 8501:8501 disease-prediction-app

📁 Project Structure

multiple-disease-prediction/
├── app.py                      # Main Streamlit application
├── models/                     # Trained ML models
│   ├── diabetes_model.pkl
│   ├── heart_disease_model.pkl
│   └── parkinsons_model.pkl
├── data/                       # Dataset files (if included)
├── notebooks/                  # Jupyter notebooks for model training
├── utils/                      # Utility functions
├── requirements.txt            # Python dependencies
├── .streamlit/
│   ├── config.toml            # Streamlit configuration
│   └── secrets.toml           # Secret keys (not in repo)
├── screenshots/               # App screenshots
├── Dockerfile                 # Docker configuration (optional)
├── .gitignore                 # Git ignore file
├── LICENSE                    # License file
└── README.md                  # This file

🧠 Models & Data

Machine Learning Models

• Diabetes Model: Trained on Pima Indian Diabetes Dataset
• Heart Disease Model: Trained on Cleveland Heart Disease Dataset
• Parkinson's Model: Trained on UCI Parkinson's Dataset

Model Performance

Disease	Algorithm	Accuracy	Precision	Recall	F1-Score
Diabetes	Logistic Regression	85%	0.83	0.87	0.85
Heart Disease	Random Forest	88%	0.86	0.89	0.87
Parkinson's	SVM	92%	0.91	0.93	0.92

Data Sources

• UCI Machine Learning Repository
• Kaggle Datasets
• Publicly available medical datasets

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Areas for Contribution:

• Adding new disease prediction models
• Improving model accuracy
• Enhancing UI/UX
• Adding data visualizations
• Writing tests
• Improving documentation

📊 Future Enhancements

• Add more disease prediction models
• Implement deep learning models
• Add data visualization charts
• Create REST API endpoints
• Add user authentication
• Implement prediction history
• Add model explainability features
• Mobile app version

⚠️ Disclaimer

This application is for educational and informational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare professionals for medical concerns.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

✍️ Author

Vinayak

• GitHub: @therealvinayak

🙏 Acknowledgments

• UCI Machine Learning Repository for datasets
• Streamlit team for the amazing framework
• Scikit-learn contributors
• Open source community

---

⭐ If you found this project helpful, please consider giving it a star! ⭐