Classification of Chronic Kidney Disease using Feature Selection Techniques - Shrivas, AK; Sahu, Sanat Kumar (International Journal of Computer Sciences and Engineering, 2025)
Classification and features selection play very important role to develop robust and computationally efficient model. In this paper, we have compared different classification techniques for classification of chronic kidney disease data. Two supervised classification learning algorithms are used to develop classifiers as Multilayer Perceptron Network (MLPN) and Radial Base Function Network (RBFN). The main focus of this research work is to reduce the number of features using different feature selection technique. We have also used five different classification techniques for select the relevant feature subsets and improve the accuracy of the classification through the Feature Selection Technique (FST). The RBFN classifier achieved the highest average percentage of performance in terms of accuracy. The results shows that both classification techniques given satisfactory accuracy rate in each different selected feature subset.