European Journal of Molecular & Clinical Medicine

Abstract

Chronic Kidney disease (CKD) is a most predominant public health concern with increasing occurrence. CKD consists of an extensive variety of path physiological processes which will be experimental along with irregular function of kidneys and progressive decrease in Glomerular Filtration Rate (GFR).In CKD prediction various data mining methods play major important role and discovering the association among effective features in this stare canister lend a hand to detect or slow progression of this CKD disease. The information is serene from the patients’ medical records. The major intention of this effort is introducing a Hybrid Filter Wrapper Embedded (HFWE) based Feature Selection (FS) to select optimal subset of features from CKD dataset. This HFWE-FS algorithm combines the procedure of filter, wrapper and embedded algorithm. Filter algorithm is executed based on the three major functions: Relief, One- R, Gain Ratio (GR) and Gini Index (GI). Wrapper algorithm is accomplished placed on the Improved Bat Algorithm (IBA) to choose analytical Attributes from the CKD dataset. Embedded algorithm is accomplished placed on the Support Vector Machine-t-statistics (SVM-t) to choose analytical attributes. The results of all feature selection algorithms are combined and named as HFWE- FS algorithm.