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

Table 8

Classification accuracies achieved with our method and other methods.

Study	Method	Accuracy (%)
Little et al. [6]	Preselection filter + exhaustive search + SVM	91.40 (bootstrap with 50 replicates)
Shahbaba and Neal [7]	Dirichlet process mixtures	87.70 (5-fold CV)
Das [8]	ANN	92.90 (hold out)
Sakar and Kursun [9]	Mutual information + SVM	92.75 (bootstrap with 50 replicates)
Psorakis et al. [10]	Improved mRVMs	89.47 (10-fold CV)
Guo et al. [11]	GP-EM	93.10 (10-fold CV)
Luukka [12]	Fuzzy entropy measures + similarity	85.03 (hold out)
Ozcift and Gulten [14]	CFS-RF	87.10 (10-fold CV)
Li et al. [13]	Fuzzy-based nonlinear transformation + SVM	93.47 (hold out)
Åström and Koker [15]	Parallel NN	91.20 (hold out)
Spadoto et al. [16]	PSO + OPF; Harmony search + OPF; Gravitational search + OPF	73.53 (hold out); 84.01 (hold out); 84.01 (hold out)
Daliri [19]	SVM with chi-square distance kernel	91.20 (50-50% training-testing)
Polat [17]	FCMFW + KNN	97.93 (50-50% training-testing)
Chen et al. [18]	PCA-FKNN	96.07 (average 10-fold CV)
Zuo et al. [20]	PSO-FKNN	97.47 (10-fold CV)
This study	SCFW-KELM	99.49 (10-fold CV)