Classification of Chronic Kidney Disease using Combination Feature Selection Techniques and Classifiers - Shrivas, AK; Sahu, Sanat Kumar (International Journal of Computer Sciences and Engineering, 2025)
The aim of the study is to predict significant features from dataset of Chronic Kidney Disease features. It represents the data in a tabular and graphical manner to form its clear understanding. This investigation helps in the crucial role of features and experimental features in the CKD dataset and their associations, their dependability for coming up with any classification system. It also shows that how CKD can be diagnosis by exploiting data mining techniques. The Data Mining algorithm is an inspirational force in detecting abnormalities in various data sets and with a good success utilized in various classification and feature selection task. The different kinds of Decision Tree-based classifiers like RF (Random Forest), J48 (C4.5), C5.0, and CART (Classification and Regression Tree) and their ensemble model have experimentally validated CKD dataset and our result is evaluated. Our result representation that the ensemble models classifier reached the most favourable performances on the identification of CKD dataset before and after the feature selection