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

Table 9

Comparison of our proposed approach with previous works.

Author	Year	Model	Dataset	ACC(%)	AMC	G-mean(%)	Sen(%)	Spec(%)
Karabatak[41]	2009	Neural network classification with association rules for reducing the dimension.	WBC	95.60	—	—	—	—
Zheng[22]	2014	Support vector machine algorithms with K-means for feature extraction	WBC	97.38	—	—	—	—
Nahato[42]	2015	Rough set indiscernibility relation method and the backpropagation neural network	WBC	98.61	—	98.60	98.76	98.57
Wang[20]	2018	SVM-based ensemble learning algorithm	WBC	97.10	—	97.17	97.11	97.23
			WDBC	97.68	—	97.09	94.75	99.49
Proposed	—	Cost-sensitive SVM with IG for feature selection	WBC	98.74	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 value for each performance.