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

Table 3

Results of KELM with different types of kernel functions in the original PD dataset without SCFW.

Kernel type	Performance metrics	Mean	SD	Max	Min
RBF_kernel	ACC (%)	95.89	4.66	100	89.74
	Sensitivity (%)	96.35	5.19	100	88.89
	Specificity (%)	95.72	5.93	100	88.00
	AUC (%)	96.04	4.06	100	90.43
	-measure	0.9724
	Kappa	0.8925
Wav_kernel	ACC (%)	94.36	4.59	100	87.18
	Sensitivity (%)	91.24	6.02	100	83.33
	Specificity (%)	95.15	5.23	100	86.21
	AUC (%)	93.19	4.56	100	88.10
	f-measure	0.9622
	Kappa	0.8425
Lin_kernel	ACC (%)	89.23	7.99	97.44	79.49
	Sensitivity (%)	66.07	22.33	90.91	41.67
	Specificity (%)	97.32	2.80	100	93.33
	AUC (%)	81.70	12.22	95.45	68.89
	-measure	0.9316
	kappa	0.6333
Poly_kernel	ACC (%)	90.77	4.29	97.44	87.18
	Sensitivity (%)	87.73	11.54	100	75.00
	Specificity (%)	91.83	5.73	96.77	82.76
	AUC (%)	89.78	5.78	98.39	82.66
	-measure	0.9375
	kappa	0.7547