An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

Table 10

Results of SCFW-KELM with different types of kernel functions in WDBC dataset.

Kernel type	Performance metrics	Mean	SD	Max	Min
RBF_kernel	ACC (%)	99.65	0.79	100	98.23
	Sensitivity (%)	99.05	2.13	100	95.24
	Specificity (%)	100	0	100	100
	AUC (%)	99.52	1.06	100	97.62
	-measure	0.9972
	Kappa	0.9925
Wav_kernel	ACC (%)	99.65	0.48	100	99.12
	Sensitivity (%)	99.10	1.24	100	97.62
	Specificity (%)	100	0	100	100
	AUC (%)	99.54	0.66	100	98.65
	-measure	0.9958
	Kappa	0.9925
Lin_kernel	ACC (%)	98.07	1.69	100	95.61
	Sensitivity (%)	94.70	5.27	100	86.11
	Specificity (%)	100	0	100	100
	AUC (%)	97.35	2.63	100	93.06
	-measure	0.9848
	Kappa	0.9582
Poly_kernel	ACC (%)	99.40	0.88	99.12	97.37
	Sensitivity (%)	95.33	2.07	97.73	93.48
	Specificity (%)	100	0	100	100
	AUC (%)	97.67	1.04	98.86	96.74
	-measure	0.9944
	Kappa	0.962