[Retracted] A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

Table 2

Classifier achievement data for each data group.

(a)

Data set	Baseline data group			MFCC data group
Performance criteria	Classifier algorithms
	Decision tree (DT)	kNN	SVM	Decision tree (DT)	kNN	SVM
Accuracy rate (%)	71.35	68.75	66.67	69.79	75	73.44
Sensitivity	0.75	0.7	0.72	0.6	0.72	0.7
Specificity	0.68	0.68	0.61	0.79	0.79	00.78
F-measurement	0.72	0.69	0.66	0.69	0.75	0.73
Kappa	0.43	0.38	0.33	0.4	0.5	0.47
Area under the ROC curve	0.71	0.71	0.68	072	0.72	0.72

(b)

Data set	Time data group			Vocal data group
Performance criteria	Classifier algorithms
	Decision tree (DT)	kNN	SVM	Decision tree (DT)	kNN	SVM
Accuracy rate (%)	66.15	67.71	72.4	61.98	64.06	63.02
Sensitivity	0.73	0.82	0.8	0.64	0.58	0.64
Specificity	0.59	0.53	0.65	0.6	0.7	0.63
F-measurement	0.65	0.65	0.72	0.62	0.64	0.63
Kappa	0.32	0.35	0.45	0.24	0.28	0.26
Area under the ROC curve	0.65	0.69	0.70	0.61	0.63	0.64

(c)

Data set	Wavelet data group			Whole data group
Performance criteria	Classifier algorithms
	Decision tree (DT)	kNN	SVM	Decision tree (DT)	kNN	SVM
Accuracy rate (%)	68.23	73.44	79.69	69.79	76.56	85.42
Sensitivity	0.81	0.76	0.97	0.6	0.81	0.94
Specificity	0.55	0.72	0.63	0.79	0.72	00.78
F-measurement	0.66	0.73	0.76	0.69	0.76	0.86
Kappa	0.36	0.47	0.59	0.4	0.53	0.72
Area under the ROC curve	0.69	0.74	0.82	0.70	0.78	0.89