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

Table 9

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

Kernel type	Performance metrics	Mean	SD	Max	Min
RBF_kernel	ACC (%)	99.34	0.91	100	98.33
	Sensitivity (%)	100	0	100	100
	Specificity (%)	98.75	1.72	100	96.67
	AUC (%)	99.37	0.86	100	98.33
	-measure	0.9964
	Kappa	0.9867
Wav_kernel	ACC (%)	99.01	0.90	100	98.36
	Sensitivity (%)	100	0	100	100
	Specificity (%)	97.84	2.02	100	95.83
	AUC (%)	98.92	1.01	100	97.92
	-measure	0.9891
	Kappa	0.98
Lin_kernel	ACC (%)	93.07	93.07	93.07	93.07
	Sensitivity (%)	98.77	98.77	98.77	98.77
	Specificity (%)	87.05	87.05	87.05	87.05
	AUC (%)	92.91	92.91	92.91	92.91
	-measure	0.9195
	Kappa	0.8591
Poly_kernel	ACC (%)	98.35	2.33	100	95.08
	Sensitivity (%)	100	0	100	100
	Specificity (%)	96.60	5.01	100	88.89
	AUC (%)	98.30	2.50	100	94.44
	-measure	0.9817
	Kappa	0.9667