Table 14.

Comparison of classification accuracy with state-of-the-art approaches for breast cancer detection.

Ref.	Models	Features	Cross-Validation	Dataset	Accuracy
[18]	GONN	9	10 folds	WBCD	98.24%, 99.63% and 100% for 50–50, 60–40, 70–30 train–test split, respectively
[13]	SMO	10	No	WBCD	96.2%
[10]	SVM, MLP	N/M	No	WBCD	99%, 99.28%
[19]	AR, NN	9	3 folds	WBCD	95.6%
[16]	SVM	30	No	WDBC + WPBC	97.0%
[17]	K-NN	9	10 folds	WBCD	99.14%
Proposed	KNN	15	Yes	WBCD	100%