Novel Ensemble Model With Genetic Algorithm and Principal Components Analysis for Classification of Chronic Kidney Disease

This study aims to appraise the key features and components of chronic kidney disease (CKD) problems in the case of the classification task. There are different types of the classifier which presented a result with low accuracy. A new combined classifier may be needed to achieve better results. Classification techniques used include k-nearest neighbors (k-NN), naive bayes (NB), and their proposed ensemble model. A feature selection technique (FST) based on genetic algorithms (GA) is presented and compared to famous dimensionality reduction techniques (DRT), namely principal component analysis (PCA). The proposed ensemble model classifiers have higher accuracy rates on the CKD dataset than the other classification models before and after applying the FST and DRT. The results demonstrated that employing FST using GA and DRT as PCA has enhanced classification accuracy. This model can be used for the identification of CKD in the early stage.