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

Table 7

The results obtained from four algorithms in the original and weighted PD dataset.

Methods	Performance metrics	Original feature space without SCFW method	Weighted feature space with SCFW method
KELM-RBF	ACC (%)	95.89 ± 4.66	99.49 ± 1.15
	Sensitivity (%)	96.35 ± 5.19	100 ± 0
	Specificity (%)	95.72 ± 5.93	99.39 ± 1.36
	AUC (%)	96.04 ± 4.06	99.69 ± 0.68
	-measure	0.9724	0.9966
	Kappa	0.8925	0.9863
	Time (s)	0.00435	0.0126
SVM	ACC (%)	95.38 ± 1.15	97.95 ± 2.15
	Sensitivity (%)	85.09 ± 10.45	96.67 ± 7.45
	Specificity (%)	98.67 ± 2.98	98.71 ± 1.77
	AUC (%)	91.88 ± 4.14	97.69 ± 3.46
	-measure	0.9699	0.9863
	Kappa	0.8711	0.9447
	Time (s)	1.24486	1.29817
KNN	ACC (%)	95.38 ± 5.25	97.43 ± 3.14
	Sensitivity (%)	92.73 ± 11.85	97.78 ± 4.97
	Specificity (%)	96.50 ± 4.38	97.38 ± 4.10
	AUC (%)	94.61 ± 6.95	97.58 ± 2.60
	-measure	0.9692	0.9828
	Kappa	0.8765	0.9431
	Time (s)	1.2847	1.3226
ELM	ACC (%)	89.23 ± 6.88	96.92 ± 4.21
	Sensitivity (%)	73.94 ± 13.18	95.78 ± 5.79
	Specificity (%)	93.35 ± 6.27	97.19 ± 4.51
	AUC (%)	83.64 ± 9.06	96.48 ± 4.36
	-measure	83.64 ± 9.06	0.9863
	Kappa	0.7078	0.9447
	Time (s)	1.1437	1.2207