AI-Enabled Intelligent Nephrology: Comparative Analysis of Artificial Intelligence Models for Robust Early Prediction and Clinical Evaluation of Chronic Kidney Disease.

Abstract

Chronic Kidney Disease (CKD) represents a major global health burden characterized by progressive and irreversible loss of renal function, often remaining asymptomatic until advanced stages. Early detection and accurate risk stratification are therefore vital to improving patient outcomes and optimizing clinical decision-making. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) have shown remarkable potential to transform nephrology by enabling predictive, personalized, and data-driven healthcare systems. This study presents a comprehensive comparative analysis of multiple AI-driven models for the robust early prediction and clinical evaluation of CKD. Using publicly available datasets such as the UCI CKD repository and validated clinical laboratory records, the proposed framework systematically evaluates the performance of traditional ML classifiers Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), and Logistic Regression (LR) against advanced deep learning architectures including Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). Data preprocessing involved normalization, missing value imputation, and feature selection based on mutual information and recursive feature elimination to enhance model generalization. Hyperparameter tuning was optimized using grid search and cross-validation techniques to mitigate overfitting and bias. The models were evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and overall classification accuracy. Experimental results reveal that ensemble-based models, particularly RF and XGBoost, achieve superior performance with over 98% accuracy and high sensitivity in identifying early-stage CKD patients. Deep learning models demonstrated strong feature-learning capability but required larger sample sizes for optimal generalization. Beyond quantitative analysis, the study integrates interpretability frameworks such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to visualize key predictors influencing model decisions, including serum creatinine, blood urea, hemoglobin, and blood pressure. These explainability mechanisms bridge the gap between algorithmic output and clinical trust, supporting transparent decision-making in nephrology practice. The findings underscore that AI-enabled intelligent systems can augment nephrologists in early CKD risk detection, disease staging, and personalized monitoring, paving the way for precision nephrology. Future work will focus on integrating federated learning and multimodal data fusion to enhance privacy, scalability, and real-world deployment within clinical environments.