An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Table 19

Comparison of the classification accuracies of various methods.

Study	Method	Accuracy ()
Little et al. (2009)	Pre-selection filter + Exhaustive search + SVM	91.4(bootstrap with 50 replicates)
Shahbaba et al. (2009)	Dirichlet process mixtures	87.7(5-fold CV)
Das (2010)	ANN	92. (hold-out)
Sakar et al. (2010)	Mutual information based feature selection + SVM	92.75(bootstrap with 50 replicates)
Psorakis et al. (2010)	Improved mRVMs	89.47(10-fold CV)
Guo et al. (2010)	GP-EM	93.1(10-fold CV)
Ozcift et al. (2011)	CFS-RF	87.1(10-fold CV)
Li et al. (2011)	Fuzzy-based non-linear transformation + SVM	93.47(hold-out)
Luukka (2011)	Fuzzy entropy measures + Similarity classifier	85.03(hold-out)
Spadoto et al. (2011)	Particle swarm optimization + OPF	73.53(hold-out)
	Harmony search + OPF	84.01(hold-out)
	Gravitational search algorithm + OPF	84.01(hold-out)
AStröm et al. (2011)	Parallel NN	91.20(hold-out)
Chen et al.(2013)	PCA-FKNN	96.07(10-fold CV)
Babu et al. (2013)	projection based learning for meta-cognitive radial basis function network (PBL-McRBFN)	99.35% (hold-out)
Hariharan et al. (2014)	integration of feature weighting method, feature selection method and classifiers	100%(10-fold CV)
Cai et al. (2017)	support vector machine (SVM) based on bacterial foraging optimization (BFO)	97.42%(10-fold CV)
This Study	CBFO-FKNN	97.89%(10-fold CV)