Table 13.

The comparison of performance between the previous studies and our work on the same dataset.

REF	Feature selection methods	The best model	Dataset	Result
[22]	RFE	RF	CKD dataset	AC = 100%
				PR = 100%
				RE = 100%
				FS = 100%
[27]	No	A hybrid model LR and RF	CKD dataset	AC = 99.94%
				E = 99.84%
				S = 99.80%
[30]	CFS	AdaBoost based on KNN	CKD dataset	AC = 98.1%
				PR = 98%
				RE = 98%
				FS = 98%
[23]	Rffs, FS, FES, BS, BES	RF	CKD dataset	AC = 98.825%
				RE = 98.04%
[24]	Cost-sensitive ensemble feature ranking	An ensemble of decision tree models	CKD dataset	AC = 97.27%
				PRC = 99.44%
				RE = 96.25%
				FS = 97.68%
[25]	No	Random subspace-based KNN	CKD dataset	AC = 100%
				RE = 100%
[26]	Genetic search algorithm	Multilayer perceptron	CKD dataset	AC = 99.75%
Our work	Relief-F	DT	CKD dataset	Cross-validation result AC = 100%, PRC = 100%, RRE = 100% FS = 100% result of testing AC = 100%, PRC = 100%, RRE = 100%, FS = 100%
		GBT Classifier	CKD dataset	Cross-validation result AC = 100%, PRC = 100%, RRE = 100%, FS = 100%; result of testing AC = 100%, PRC = 100%, RRE = 100%, FS = 100%