Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy

Abstract

Chronic kidney disease (CKD) affects millions of people worldwide, greatly reducing their quality of life and creating serious economic, social, and medical problems. Some automated diagnosis methods can detect chronic renal disease. In-depth studies on data mining techniques have recently focused on accuracy in the diagnosis of chronic renal illnesses, either by taking advantage of the disease's simplicity or doing feature selection in addition to pre-processing. In order to handle the unbalanced dataset in this work, Synthetic Minority Over Sampling Technique (SMOTE) is used during pre-processing. For this investigation, 400 data from the publicly accessible UCI machine learning (ML) repository are used. For the implementation, both homogeneous and heterogeneous ensemble classifiers which combine two separate classifiers have been used. Different machine learning (ML) techniques, such as the Classification and Regression Tree (CART), Adaboost classifier, Decision Tree (DT), Reduced Error Pruning Tree, Alternating Decision Tree, and Random Forests Algorithm and their ensembles with a significant reduction in entropy, are used to perform the classification. With a 99.12% accuracy rate and a 99.10% f1 score, the homogeneous classifier Adaboost-Random Forest outperforms other models in the prediction of CKD.