nefrikinoso: Early Chronic Kidney Disease Prediction System

Overview

nefrikinoso is a machine learning project focused on predicting Chronic Kidney Disease (CKD). It utilizes various machine learning models to provide accurate CKD predictions and includes tools for model evaluation and user interaction.

Features

• Preprocesses and prepares the CKD dataset.
• Trains and evaluates model performance using relevant metrics.
• Offers a web interface for user interaction and predictions.
• Provides an API endpoint for making predictions programmatically.
• Generates visualizations for model evaluation and feature analysis.

• Includes the following implementations:
  • Novel 🤖 Voting Ensemble
  • Novel 🤖 Stacked Ensemble Learning
  • XGBoost
  • SVM
  • Decision Tree
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Random Forest
  • Gradient Boosting
  • CatBoost
  • Neural Network

Usage (Docker)

docker run netherquark/nefrikinoso

Installation (Regular)

1. Clone the repository.
2. Navigate to the project directory.
3. Install the required dependencies using pip install -r requirements.txt.

Usage (Regular)

Training and Visualisation

Run the models and generate visualisations using python main.py.

Running the Web Application

Execute the app.py script to launch the web interface for CKD prediction.

Using the API

The API endpoint /api/predict accepts patient data for CKD prediction.

Evaluating Models

Run the main.py script to evaluate and compare the performance of the implemented machine learning models.

Dependencies

• Python 3.11
• pandas
• matplotlib
• joblib
• seaborn
• scikit-learn
• CatBoost
• XGBoost
• Flask
• GUnicorn

License

This project is licensed under the GNU GPLv3 License. Refer to LICENSE for more details.