Comparative Study of Classification Models with Genetic Search Based Feature Selection Technique

Literature Survey

This part consists of reviews of various technical and related articles on machine learning techniques applied to predict kidney disease.

The two types (Polat et al., 2017) of feature selection methods, i.e., wrapper and filter approach have been used to diagnose CKD. In wrapper approach, a classifier subset evaluator with the greedy stepwise search engine and wrapper subset evaluator with the Best First Search(BFS) engine were used. In filter approach, correlation feature selection subset evaluator with a greedy stepwise search engine and filtered subset evaluator with the BFS engine were used. Results showed that the Support Vector Machine (SVM) classifier has used filtered subset evaluator with the BFS engine feature selection method gives a higher accuracy rate (98.5%) in the diagnosis of CKD.

A number of different ML classifiers (Subas et al., 2017) Artificial Neural Network(ANN), SVM, k-Nearest Neighbor, C4.5 and Random Forest (RF) have experiment validated to a real data set, taken from the UCI Machine Learning Repository. The result reveals that the random forest (RF) classifier reaches the maximum performances on the classification of CKD.