Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

Table 10

Classification accuracies obtained by our method and other classifiers for diabetes dataset.

Author (year)	Method	Classification accuracy (%)
Şahan et al. (2005) [14]	AWAIS (10-fold CV)	75.87
Polat and Güneş (2007) [15]	Combining PCA and ANFIS	89.47
Polat et al. (2008) [16]	LS-SVM (10-fold CV)	78.21
	GDA-LS-SVM (10-fold CV)	82.05
Kahramanli and Allahverdi (2008) [17]	Hybrid system (ANN and FNN)	84.2
Patil et al. (2010) [18]	Hybrid prediction model (HPM ) with reduced dataset	92.38
Isa and Mamat (2011) [19]	Clustered-HMLP	80.59
Aibinu et al. (2011) [20]	AR1 + NN (3-fold CV)	81.28
Our study	ABCFS + SVM (train: 75%-test: 25%)	86.97
	ABCFS + SVM (10-fold CV)	79.29