Table 3 Performance analysis of used algorithms on breast cancer classification problem.

	Algorithms	Accuracy (%)	MSE	SD
	ABCNN	85.31	0.1080	0.0195
	CSNN	91.61	0.0626	0.0107
	ERN	98.00	0.0140	0.0130
	LM	95.20	0.0280	0.0142
²⁸	DNN-RBM	98.24	–	–
⁶⁴	ABCFLNN	94.74	0.2627	–
⁶⁵	ABC-BP	92.02	0.184	0.459
	ABC-LM	93.83	0.0139	0.0010
	ABCNN	88.96	0.014	0.0002
	BPNN	90.71	0.271	0.017
	CSBPERN	97.37	0.00072	0.0004
¹⁹	CAPSO-MLP	82.50	0.175	–
	PSO-MLP	80	0.179	–
	GSA-MLP	80	0.190	–
	ICA-MLP	80	0.180	–
⁶⁶	bSCWDTO-KNN	97.64	0.369	0.2763
	bDTO-KNN	92.74	0.381	0.2810
	bPSO-KNN	95.01	0.382	0.2851
	bWAO-KNN	93.98	0.402	0.2914
	bGWO-KNN	94.76	0.381	0.2802
	bMVO-KNN	94.21	0.380	0.2821
	bSBO-KNN	95.43	0.392	0.2988
	bGWOGA-KNN	94.58	0.404	0.2916
	bFA-KNN	94.82	0.392	0.2810
	bGA-KNN	96.12	0.387	0.2832
	bSC-KNN	93.29	0.373	0.2800
⁶⁷	bGWDTO-KNN	95.23	0.245	0.1365
⁶⁸	bGWDTO-KNN	71.64	0.5811	0.40078
Proposed	RMONN	98.60	0.0184	0.0022
	RMOBPERN	98.60	0.042	0.0001
	RMOLMBP	97.20	0.049	0.00012
	RMOLM	96.50	0.042	0.00031

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