Chronic Kidney Disease Prediction
Overview:
This project utilizes Flask, a Python micro web framework, to deploy a machine learning model for predicting chronic kidney disease (CKD). The model is trained on a dataset consisting of 400 samples using the XGBoost classifier.
Features:
- Predicts the likelihood of chronic kidney disease based on various medical parameters.
- User-friendly interface for inputting medical data and obtaining predictions.
- Seamless deployment using Flask, enabling easy access for healthcare professionals and patients alike.
Usage:
-
Clone the repository to your local machine:
git clone <repository_url> -
Install the required dependencies:
pip install -r requirements.txt -
Run the Flask application:
python app.py -
Access the application through your web browser at the provided URL.
Model Details:
- Algorithm: XGBoost Classifier
- Training Data: 400 samples
- Features: Various medical parameters including age, blood pressure, serum creatinine, etc.
- Performance Metrics: Accuracy, precision, recall, F1-score, ROC-AUC
Contributing:
Contributions to improve the model's performance or enhance the application's features are welcome. Feel free to submit pull requests or open issues.
Disclaimer:
This application is intended for educational and informational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Consult a qualified healthcare provider for any medical concerns.
Author:
[Mohammed Haseebuddin / [email protected]]
This README provides a brief overview of the Chronic Kidney Disease Prediction project, its features, usage instructions, model details, and other relevant information. Feel free to customize it further based on your project's specific requirements and preferences.