🧠 Chronic Kidney Disease Prediction Using Machine Learning

📌 Project Overview

This project focuses on predicting Chronic Kidney Disease (CKD) using machine learning techniques. Early detection of CKD can significantly improve patient outcomes by enabling timely medical intervention.

The model is trained on the UCI Machine Learning Repository CKD dataset, which includes 24 clinical and demographic features such as blood pressure, hemoglobin, albumin levels, and more.

The goal is to classify whether a patient has CKD (Yes / No) using various ML algorithms and compare their performance.

---

🎯 Objectives

• Perform data preprocessing and cleaning
• Train multiple machine learning models
• Evaluate models using performance metrics
• Deploy the best model with a user-friendly interface

---

🗂️ Project Structure

CKD-Prediction/
│── data/
│── notebooks/
│── src/
│── models/
│── results/
│── main.py
│── requirements.txt
│── README.md

---

⚙️ Tech Stack

• Programming Language: Python
• Libraries: pandas, numpy, matplotlib, seaborn, sklearn
• GUI: tkinter

---

🔍 Workflow

1. Data Preprocessing

• Handling missing values
• Encoding categorical features
• Feature scaling and normalization

2. Model Training

Models used:

• Logistic Regression
• Decision Tree
• Random Forest
• Neural Network (Keras)

3. Model Evaluation

Evaluation metrics:

• Accuracy
• Precision
• Recall
• F1 Score

4. Deployment

• GUI-based application built using tkinter

• Allows users to:

  • Train model
  • Test model on new data

---

📊 Results

• Compared multiple models to identify best performance
• Decision Tree has achieved the highest accuracy from 87% to 93%

---

💻 Installation & Setup

Step 1: Clone Repository

git clone https://github.com/Bikash07-git/chronic-kidney-disease-prediction.git
cd chronic-kidney-disease-prediction

Step 2: Create Virtual Environment

python -m venv venv

Activate Environment

• Windows:

venv\Scripts\activate

• Linux/Mac:

source venv/bin/activate

---

Step 3: Install Dependencies

pip install -r requirements.txt

---

Step 4: Run Application

python main.py

---

🖥️ Usage

• Launch the application

• Choose:

  • Train Model → trains and saves model
  • Test Model → predicts CKD on new input

---

📈 Future Improvements

• Add web deployment (Flask / FastAPI)
• Improve model performance using feature engineering
• Add real-time prediction API
• Use advanced models like XGBoost / LightGBM

---

📜 License

This project is licensed under the MIT License.

---

🙏 Acknowledgments

• Dataset: UCI Machine Learning Repository
• Contributors: Open-source community

---