Enhancing Chronic Kidney Disease Prediction : A Hybrid Approach Combining Logistic Regression and Random Forest Models

Abstract

This study investigates the use of Machine Learning (ML) models for Chronic Kidney Disease (CKD) prediction, comparing Logistic Regression with L1 and L2 regularization, Random Forest , and a Hybrid Voting Classifier. The models were evaluated using performance metrics including accuracy, precision, recall, and F1-score, with the hybrid model demonstrating the highest accuracy of 99 percent, followed by Random Forest at 98 percent. Logistic Regression models achieved accuracies of 97 percent and 98 percent , with slight variations in recall for different classes. Cross-validation and learning curve analyses indicated minimal overfitting in ensemble models. These results emphasize the potential of ML models for accurate CKD prediction, suggesting further research into model optimization and data preprocessing techniques.