Table 1.

Breast cancer diagnosis methods (providers).

Provider #	Method	Accuracy, %
1	CfSa + SVMb [35]	87.84
2	Filtered + SVM [35]	87.84
3	CfS + logistic regression [35]	95.95
4	Filtered + logistic regression [35]	96.62
5	BPSOc-2Stage [36]	92.98
6	PSOd (4-2) [36]	93.98
7	KPe-SVM [37]	97.55
8	RFEf-SVM [37]	95.25
9	FSVg [37]	95.23
10	Fisher + SVM [38]	94.70
11	Self-training [38]	85.12
12	Random co-training [38]	83.54
13	Rough co-training [38]	88.63
14	LDAh [39]	97.19
15	C4.5 [39]	94.06
16	DIMLPi [39]	96.92
17	SIMj [39]	98.26
18	MLPk [39]	97.43
19	PSO-KDEl (1) [40]	98.45
20	PSO-KDE (2) [40]	98.45
21	GAm-KDE (2) [40]	98.45
22	Fisher + PFree Batn + LSo-SVM [41]	100

^aCfS: correlation-based feature selection.

^bSVM: support vector machine.

^cBPSO: binary particle swarm optimization.

^dPSO: particle swarm optimization.

^eKP: kernel-penalized SVM (KP-SVM). 

^fRFE: recursive feature elimination.

^gFSV: feature selection concave.

^hLDA: linear discriminant analysis.

ⁱDIMLP: discretized interpretable multilayer perceptron. 

^jSIM: similarity classifier.

^kMLP: multilayer perceptron. 

^lKDE: kernel density estimation. 

^mGA: genetic algorithm.

ⁿPFree Bat: parameter-free bat optimization algorithm.

^oLS: least square support vector machine.