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

Table 5

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

Kernel type	Performance metrics	Mean	SD	Max	Min
RBF_kernel	ACC (%)	99.49	1.15	100	97.44
	Sensitivity (%)	100	0	100	100
	Specificity (%)	99.39	1.36	100	96.97
	AUC (%)	99.69	0.68	100	98.48
	-measure	0.9966
	Kappa	0.9863
Wav_kernel	ACC (%)	96.92	2.15	100	94.87
	Sensitivity (%)	98.46	3.44	100	92.31
	Specificity (%)	96.54	2.39	100	93.33
	AUC (%)	97.50	2.18	100	94.23
	-measure	0.9793
	Kappa	0.9194
Lin_kernel	ACC (%)	96.92	2.15	100	94.87
	Sensitivity (%)	90.43	8.85	100	81.82
	Specificity (%)	99.29	1.60	100	96.43
	AUC (%)	94.86	3.99	100	90.91
	-measure	0.9798
	Kappa	0.9147
Poly_kernel	ACC (%)	97.43	2.56	100	94.87
	Sensitivity (%)	96.67	7.45	100	83.33
	Specificity (%)	97.37	3.61	100	93.10
	AUC (%)	97.02	3.42	100	91.67
	-measure	0.9828
	Kappa	0.9323