Chronic Kidney Disease (CKD) Prediction Web Application

This repository contains a web application designed to predict the likelihood of Chronic Kidney Disease (CKD) based on patient data. It utilizes a machine learning model trained on the Kaggle CKD dataset and provides a user-friendly interface for making predictions.

Table of Contents

• Project Overview
• Features
• Installation
• Usage
• Project Structure
• Model Details
• Contributing
• Acknowledgements

Project Overview

This project aims to provide a simple and accessible tool for predicting CKD. By inputting relevant patient data, users can obtain a prediction indicating whether the patient is likely to have CKD. The application is built using Flask for the backend and HTML/JavaScript for the frontend, with a machine learning model trained using scikit-learn.

Features

• User-friendly Web Interface: An intuitive interface for inputting patient data.
• CKD Prediction: Predicts the likelihood of CKD based on input features.
• Sample Data Buttons: Buttons to quickly populate the form with sample data for testing.
• Clear Output Display: Displays the prediction result clearly on the page.
• Error Handling: Graceful handling of input errors with user-friendly error messages.
• Clear Output Button: A button to clear the prediction results.

Installation

1. Clone the Repository:

   git clone [https://github.com/Siddharth0207/CKD-Complete-Version.git](https://www.google.com/search?q=https://github.com/Siddharth0207/CKD-Complete-Version.git)
   cd CKD-Complete-Version

2. Create a Virtual Environment (Recommended):

   python -m venv venv
   source venv/bin/activate  # On Linux/macOS
   venv\Scripts\activate  # On Windows

3. Install Dependencies:

   pip install -r requirements.txt

Usage

1. Run the Flask Application:

   python application.py

2. Open the Web Application:

   Open your web browser and navigate to http://0.0.0.0:5000/.

3. Input Patient Data:

   Enter the required patient data in the input fields.

4. Make a Prediction:

   Click the "Predict your CKD Stage" button to get the prediction.

5. Use Sample Data:

   Click the "Fill CKD Sample Data" or "Fill Not CKD Sample Data" buttons to populate the form with sample data for testing purposes.

6. Clear Output:

   Click the "Clear Output" button to remove the prediction result.

Project Structure

CKD-Complete-Version/ ├── .dvc/ # Data Version Control files ├── .ebextensions/ # AWS Elastic Beanstalk configuration │ └── python.config ├── .github/ # GitHub Actions workflows (if any) ├── artifacts/ # Artifacts from training/processing ├── catboost_info/ # CatBoost model info (if used) ├── data_science_project.egg-info/ # Python package info ├── logs/ # Application logs ├── notebook/ # Jupyter notebooks for exploration/training ├── src/ # Source code directory ├── templates/ # HTML templates ├── venv/ # Virtual environment (if used) ├── .dvcignore # DVC ignore file ├── .gitignore # Git ignore file ├── application.py # Flask application file ├── ckd.py # Main application logic ├── Dockerfile # Docker configuration ├── README.md # Project documentation ├── requirements.txt # Project dependencies ├── setup.py # Setup script for packaging ├── template.py # Template file (if used)

Model Details

The machine learning model used in this application is trained on the Kaggle CKD dataset. It utilizes a KNN or Random Forest and is trained to predict the likelihood of CKD based on patient features. The model is saved as model.pkl in the artifacts/ directory.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request.

Acknowledgements

• The CKD dataset. [https://www.kaggle.com/datasets/mansoordaku/ckdisease]
• The CatBoost library for efficient and scalable machine learning. [https://catboost.ai/]
• The Flask web framework for building web applications. [https://flask.palletsprojects]
• CookieCutter for project templating. [https://github.com/cookiecutter/cookiecutter-pyp]
• DVC for data version control. [https://dvc.org/]
• DagsHub for data science project management. [https://dags.ai/]
• mlflow for model tracking and deployment. [https://mlflow.org/]
• Azure Machine Learning for model deployment. [https://azure.microsoft.com/en-us/services/machine-learning/]
• GitHub Actions for continuous integration and deployment. [https://github.com/features/actions]

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