What question did this study set out to answer?

The aim is to develop machine learning models for chronic kidney disease prediction and improve their interpretability using explainable AI techniques.

June 30, 2026Open Access

View Full Paper

Toward Explainable Precision Nephrology: Machine Learning-Based Chronic Kidney Disease Prediction

Key Points

The aim is to develop machine learning models for chronic kidney disease prediction and improve their interpretability using explainable AI techniques.
Developed ML models using a dataset of 380 instances with 20 clinical attributes.
Implemented various algorithms including KNN, C5.0, and L1-regularized SVM, and utilized techniques like SMOTE for class imbalance.
Evaluated model performance using metrics like accuracy, precision, F1 Score, and AUC.
KNN achieved 94.74% accuracy without SMOTE; C5.0 obtained 92.98% accuracy with SMOTE.
L1-regularized SVM achieved 89.47% accuracy using highly correlated features.
Feature selection and resampling methods enhanced model robustness without significant performance loss.

Abstract

Background/Objectives: Chronic kidney disease (CKD) is an incurable and progressive condition; if diagnosed at an early stage, it would significantly reduce the risk of complications and enhance the outcomes for the patient. Methods: In this study, a custom dataset of 380 instances and 20 clinical attributes was used to develop and evaluate the machine learning (ML) models for reliable CKD prediction and to enhance the interpretability using explainable artificial intelligence (XAI) techniques. Artificial neural networks, C5.0, CHAID, logistic regression, linear support vector machines (L1 and L2 regularization), k-nearest neighbors (KNN), random tree, and deep neural networks were implemented. Correlation-based methods, recursive feature elimination, and LASSO were used for feature selection. SMOTE and SMOTETomek resampling techniques were used to address class imbalance. Three experimental set-ups were considered: (i) using SMOTETomek, (ii) with and without SMOTE, and (iii) grouped features according to the strength of correlation (high, moderate, low). Accuracy, precision, recall, F1 Score, AUC, and Gini index were used to evaluate the model’s performance. The pipeline was implemented in Python using the scikit-learn and imbalanced-learn packages. Results: Using SHAP and LIME, model interpretability was improved, with the KNN classifier obtaining the highest accuracy of 94.74% without SMOTE, and the C5.0 model obtained the highest accuracy of 92.98% with SMOTE. In the feature-group experiments, the L1-regularized linear SVM achieved high accuracy (89.47%) with highly correlated features. In general, both resampling methods improved model robustness, and feature selection methods reduced the model’s dimensionality with little loss in performance. Conclusions: The ML framework proposed is promising in predicting CKD with high accuracy and interpretability with relevance. By combining feature selection with class balancing and explainable AI, the model’s performance improves, and its clinical trustworthiness is enhanced. The results indicate the potential in using ML-based decision support systems for early-stage CKD diagnosis and personalized healthcare.

KI fragen

Bookmark

View Full Paper

Cite This Study

Qureshi et al. (Sat,) studied this question.

synapsesocial.com/papers/6a435c38759b888809a52a86 https://doi.org/https://doi.org/10.3390/biomedicines14071459

KI fragen

Bookmark

View Full Paper