Diabetes Prediction System

A machine learning system that predicts diabetes risk with 85% accuracy, featuring an intuitive web interface with detailed risk analysis and personalized health recommendations.

Features

• 🎯 Accurate Predictions: XGBoost model with 85% accuracy
• 💻 User-Friendly Interface: Clean Streamlit web app
• 📊 Detailed Analysis: Risk factors breakdown with visual indicators
• 💡 Personalized Recommendations: Actionable health advice
• 📱 Responsive Design: Works on desktop and mobile
• 🔍 Model Interpretability: Feature importance visualization

Installation

Prerequisites

• Python 3.8+
• pip package manager

Steps

1. Clone the repository:

git clone https://github.com/mdsaad31/diabetes-prediction.git
cd diabetes-prediction

2. Install dependencies:

pip install -r requirements.txt

3. Run the Streamlit app:

streamlit run app/diabetes_app.py

The app will launch in your default browser at http://localhost:8501

Usage

1. Fill in your health metrics in the web form
2. Get your risk assessment with probability score
3. Review detailed analysis of contributing factors
4. Follow personalized recommendations for risk reduction

📊 Model Performance

The trained XGBoost model achieved the following performance metrics on the test dataset:

Metric	Score
Accuracy	85.2%
AUC (ROC)	0.91
Precision	0.83
Recall	0.76
F1 Score	0.79

API Documentation

For developers wanting to integrate with the prediction model:

import joblib
import numpy as np
import os

# Load model and scaler using relative paths
model_path = os.path.join(os.path.dirname(__file__), '..', 'models', 'diabetes_xgb_model.pkl')
scaler_path = os.path.join(os.path.dirname(__file__), '..', 'models', 'diabetes_scaler.pkl')

model = joblib.load(model_path)
scaler = joblib.load(scaler_path)

# Prepare input data (example)
input_data = np.array([[6, 148, 72, 35, 0, 33.6, 0.627, 50]])

# Standardize and predict
scaled_data = scaler.transform(input_data)
prediction = model.predict(scaled_data)
probability = model.predict_proba(scaled_data)[:, 1]

🤝 Contributing

We welcome contributions from the community to improve this project. To contribute:

1. Fork the repository
2. Clone your forked repo:

   git clone https://github.com/your-username/diabetes-prediction.git

3. Create a new branch for your feature or fix:

   git checkout -b feature/your-feature-name

4. Make your changes and commit them:

   git commit -m "Add your message"

5. Push to your branch:

   git push origin feature/your-feature-name

6. Open a Pull Request and briefly describe your changes: Please make sure your code follows the existing style and passes all tests. Refer to the code of conduct for respectful collaboration.

🙏 Acknowledgments

• Pima Indians Diabetes Dataset – for providing the data used
• Streamlit – for powering the interactive web interface
• XGBoost – for the machine learning model
• Scikit-learn – for preprocessing and evaluation support
• All open-source contributors and libraries that made this project possible

📬 Contact

If you have any questions, suggestions, or feedback, feel free to reach out:

Md Saad
📧 Email: [email protected]
💼 GitHub: @mdsaad31

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