Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis

Table 10

Comparison of our proposed method with conventional classification models.

Method	Dataset	ACC(%)	AMC	G-mean(%)	Sen(%)	Spec(%)
SVM(RBF)	WBC	96.58	0.132	96.67	96.35	97.05
	WDBC	95.91	0.251	95.15	97.37	92.98
PSO-SVM(RBF)	WBC	95.61	0.307	94.37	97.82	91.04
	WDBC	97.66	0.234	97.05	100	94.20
BP neural network	WBC	94.11	0.324	92.20	93.30	91.30
	WDBC	94.72	0.526	92.93	100	86.41
LVQ neural network	WBC	91.56	0.627	87.88	96.55	80.00
	WDBC	92.75	0.724	90.26	100	81.48
3-NN	WBC	91.10	0.485	90.90	92.50	89.30
	WDBC	92.60	0.602	91.42	97.50	85.71
Decision Tree	WBC	96.64	0.162	96.63	97.84	95.45
	WDBC	95.65	0.304	95.27	97.50	93.10
Random Forest	WBC	96.49	0.140	96.49	96.33	96.66
	WDBC	97.53	0.145	96.82	97.82	95.83
Proposed	WBC	98.04	0.064	98.13	97.88	98.38
	WDBC	98.83	0.129	97.35	99.01	95.71

Note: the symbol of “” represent the optimal results for each performance.