Which algorithms are used in this CKD risk prediction project?

The system implements and compares three algorithms: Random Forest, XGBoost, and Logistic Regression. Random Forest achieved the best performance with 97.5% accuracy and 0.9958 ROC-AUC.

What is the accuracy of the CKD risk prediction model?

The Random Forest model achieves 97.5% accuracy with an F1-score of 0.975 and ROC-AUC of 0.9958, demonstrating excellent predictive capability for CKD risk assessment.

What features are used for CKD risk prediction?

The system uses 24 clinical features including serum creatinine, hemoglobin, blood urea nitrogen, blood pressure, albumin, and engineered features like combined risk index and kidney function score.

Who can benefit from this CKD risk prediction system?

Healthcare providers, nephrologists, primary care physicians, clinical researchers, and public health professionals can use this system for early CKD screening, risk stratification, and clinical decision support.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Chronic Kidney Disease Risk Prediction Using Machine Learning

The Chronic Kidney Disease Risk Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts CKD risk using patient clinical data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Random Forest, XGBoost, and Logistic Regression with leak-free SMOTE cross-validation, achieving 97.5% accuracy and 0.9958 ROC-AUC.

The system utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning Repository containing 400 patient records with 24 clinical features including demographic information, laboratory test results, and clinical examination findings. It provides both single-patient risk assessment and batch prediction capabilities, making it suitable for clinical decision support. The system identifies key predictive features including serum creatinine, hemoglobin levels, blood pressure, blood urea nitrogen, and albumin levels.

Python 3.8+ Machine Learning Random Forest XGBoost Logistic Regression Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model CKD Risk Prediction
Random Forest (97.5% Accuracy)
XGBoost (96.9% Accuracy)
Leak-free SMOTE Cross-Validation
Feature Engineering (7 derived features)
Interactive Visualizations
Real-time Risk Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

🌲 Random Forest

Ensemble learning with 300 trees, max_depth=12, class_weight='balanced'

🎯 Accuracy: 97.5%

⚡ XGBoost

Gradient boosting with 250 estimators, max_depth=5, learning_rate=0.05

🎯 Accuracy: 96.9%

📊 Logistic Regression

L2-regularized linear model with C=0.5, class_weight='balanced'

🎯 Accuracy: 93.8%

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV AUC: 0.9875

Methodology & Workflow

1 Data Collection
400 patient records with 24 clinical features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Random Forest, XGBoost, Logistic Regression with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time risk prediction

Model Performance Comparison

Metric	Random Forest	XGBoost	Logistic Regression	Best
Test Accuracy	97.5%	96.9%	93.8%	Random Forest
Test Precision	97.5%	97.5%	93.8%	Random Forest
Test Recall	97.5%	96.9%	93.8%	Random Forest
Test F1-Score	97.5%	96.9%	93.8%	Random Forest
Test ROC-AUC	99.6%	99.4%	98.8%	Random Forest
CV Mean ROC-AUC	98.8%	98.5%	95.4%	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial CKD Dataset (400 samples) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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Complete Source Code

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