Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease

Table 3

Comparison of classifiers for CKD.

S. no	Authors	Year	Input data	Disease type	Tools	Classifiers	Cross-validation	Accuracy
1	Padmanaban and Parthiban [8]	2016	Diabetic patients	CKD	WEKA, YALE	Naïve Bayes	10 folds	86%
			UCI machine learning			Decision tree		91%
2	Charleonnan et al. [9]	2016	Clinical data	CKD	WEKA, MATLAB	SVM	5 folds	98.3%
						Logistic regression		96.55%
						Decision tree		94.81%
						KNN		98.1%
3	Ghosh et al. [7]	2020	Apollo Hospitals India	CKD	Python	SVM	5 folds	99.56%
						AB		97.91%
						LDA		97.91%
						GB		99.80%
4	Fu et al.. [10]	2018	UCI repository (CKD dataset)	CKD	Python	RPART	No cross-validation	98.2%
						SVM		97.3%
						LOGR		99.4%
						MLP		99.5%
5	Devika et al. [11]	2019	UCI repository (CKD dataset)	Chronic renal disorder	C Sharp	Naïve Bayes	No cross-validation	99.63%
						KNN		87.78%
						Random forest		99.84%
6	Revathy et al. [12]	2019	UCI repository (CKD dataset)	CKD	Python	Decision tree	No cross-validation	94.16%
						SVM		98.33%
						Random forest		99.16%
7	Nishat et al. [14]	2021	Learning repository of University of California, Irvine	CKD	Python	CNN	No cross-validation	78%
						LR		98.25%
						DT		99%
						RF		99.75%
						SVM		85%
						NB		96.5%
						MLP		81.25%
						QDA		37.5%
8	Rabby et al. [13]	2019	UCI repository (CKD dataset)	CKD	Python	K-nearest neighbor	No cross-validation	71.25%
						RF		98.75%
						SVM		97.50
						GNB		100%
						AB		98.75%
						DT		100%
						LDA		97.50%
						GB		98.75
						LR		97.50%
						ANN		65%
9	Pouriyeh et al. [15]	2020	UCI repository (CKD dataset)	CKD	Python	RF	10 folds	97.12%
						ANN		94.5%
10	Jabber et al. [16]	2020	UCI repository (CKD dataset)	CKD	Python	Decision tree	No cross-validation	96.79%
						Logistic regression		97.86%
						Naïve Bayes		97.33%
						Random forest		98.93%
11	Bmc [17]	2013	UCI repository	Diabetic kidney disease	MATLAB	SVM	No cross-validation	0.91
						PLS		0.83
						FFNN		0.85
						RPART		0.87
						Random forest		0.91
						Naïve Bayes		0.86
						C5.0		0.90
12	Ramya and Radha [18]	2016	UCI repository	Chronic kidney disease	R	BP	No cross-validation	80.4
						RBF		85.3
						Random forest (RF)		78.6
13	Kumar [19]	2016	UCI repository	CKD	MATLAB	RF	No cross-validation	95.67
						SMO		90
						Naïve Bayes		87.64
						RBF		83.78
						MLPC		89
						SLG		87
14	Basarslan and Kayaalp [20]	2019	UCI repository	Chronic kidney disease	MATLAB	K-nearest neighbor	No cross-validation	97
						Naïve Bayes		96.5
						LR		97.56
						RF		99
15	Dowluru and Rayavarapu [21]	2012	UCI repository	Kidney stone	WEKA tool	Naïve Bayes classification	No cross-validation	0.99
						Logistic regression		1.00
						J48 algorithm		0.97
						Random forest		0.98
					Orange tool	Naïve Bayes		0.79
						KNN		0.7377
						Classification tree		0.9352
						C4.5		0.9352
						SVM		0.9198
						Random forest		0.9352

Bold values represent the highest accuracy in the relevant paper.