Chronic Kidney Disease Prediction Using Machine Learning

Table of Contents

• Chronic Kidney Disease Prediction Using Machine Learning
  • Table of Contents
  • Project Overview
  • Prerequisites
  • Project Structure
  • Installation
    • Step 1: Clone the Repository
    • Step 2: Create a Virtual Environment
    • Step 3: Install Python Libraries
    • Step 4: Run the Application
  • Usage
  • Conclusion
  • License
  • Acknowledgments

Project Overview

This project utilizes the kidney disease dataset available in the UCI Machine Learning Repository to train and test various classification models for predicting chronic kidney disease. The dataset contains 24 features, including demographic information, clinical measurements, and disease history of patients. The target variable is a binary classification indicating the presence or absence of chronic kidney disease.

Prerequisites

The following Python libraries are required for this project:

• tkinter
• pandas
• numpy
• matplotlib
• seaborn
• sklearn
• keras
• tensorflow

Project Structure

The project consists of the following main components:

1. Data Preprocessing: Data cleaning, feature selection, and normalization are performed to prepare the dataset for training.
2. Model Training: Various classification models, including logistic regression, decision trees, random forests, and neural networks, are trained and compared.
3. Model Evaluation: The performance of the trained models is evaluated using various metrics, including accuracy, precision, recall, and F1 score.
4. Model Deployment: The best-performing model is deployed as a web application using tkinter for user interaction.

Installation

To run this project, follow these steps:

1. Clone the repository to your local machine.
2. Create a virtual environment and activate it.
3. Install the required Python libraries using pip.
4. Run the main.py script to start the application.

Step 1: Clone the Repository

Clone the repository to your local machine using the following command:

git clone https://github.com/your-username/chronic-kidney-disease-prediction.git

Step 2: Create a Virtual Environment

Create a virtual environment and activate it using the following commands:

python -m venv venv
source venv/bin/activate

Step 3: Install Python Libraries

Install the required Python libraries using the following command:

pip install -r requirements.txt

Step 4: Run the Application

Run the main.py script to start the application:

python main.py

Usage

Upon starting the application, you will be presented with a user interface that allows you to choose between training the project and testing the project.

• Train Project: This option will train the machine learning models using the provided dataset. The trained model will be saved to disk for later use.
• Test Project: This option will allow you to test the performance of the trained model using a new dataset. The predicted results will be displayed on the screen.

Conclusion

This project demonstrates the use of machine learning techniques to predict chronic kidney disease using various classification models. By comparing the performance of different models, this project provides a baseline for further exploration and improvement.

License

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

Acknowledgments

The dataset used in this project is available in the UCI Machine Learning Repository and was donated by Dr. S. Charoenkwan, Department of Medicine, Faculty of Medicine,