ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach

Abstract

Chronic Kidney Disease (CKD), the gradual loss and irreversible damage of the kidney’s functionality, is one of the leading contributors to death and causes about 1.3 million people to die annually. It is extremely important to slow down the progression of kidney deterioration to prevent kidney dialysis or transplant. This study aims to leverage machine learning algorithms and ensemble models for early detection of CKD using the “Chronic Kidney Disease (CKD15)” and “Risk Factor Prediction of Chronic Kidney Disease (CKD21)” datasets from the UCI Machine Learning Repository. Two encoding techniques are introduced to combine the datasets, i.e., Discrete Encoding and Ranged Encoding, resulting in Discrete Merged and Ranged Merged datasets. The preprocessing stage employs normalization, class balancing with synthetic oversampling, and five feature selection techniques, including RFECV and Pearson Correlation. This work proposes a novel Tri-phase Ensemble technique combining Voting, Bagging, and Stacking approaches and two other ensemble models: Multi-layer Stacking and Multi-layer Blending classifiers. The investigation reveals that, for the Discrete Merged dataset, the novel Tri-phase Ensemble and Multi-layer Stacking with layers interchanged achieves an accuracy of 99.5%. For the Ranged Merged dataset, AdaBoost attains an accuracy of 97.5%. Logistic Regression accomplishes an accuracy of 99.5% in validating with the discrete dataset, whereas for validating with the ranged dataset, both Random Forest and SVM achieve 100% accuracy. Finally, to interpret and understand the behavior and prediction of the model, a LIME explainer has been utilized.