Table 4.

Comparisons of Methods for ALS, Control, Neurological Control and Parkinson Disease.

		ACC	GM	ERR	PREC	SENS	SPEC	F-M	MCC	YI	Kappa
ALS	Bayesian Network	1.000	1.000	0.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
	Neural Network	0.985	0.985	0.015	1.000	0.971	1.000	0.985	0.971	0.971	0.971
	Logistic Regression	0.975	0.975	0.025	1.000	0.951	1.000	0.975	0.952	0.951	0.951
	Naive Bayes	0.971	0.970	0.029	0.962	0.981	0.960	0.971	0.941	0.941	0.941
	J48	0.980	0.980	0.020	1.000	0.961	1.000	0.980	0.962	0.961	0.961
	SVM	0.980	0.980	0.020	1.000	0.961	1.000	0.980	0.962	0.961	0.961
	Kstar	0.975	0.976	0.025	0.990	0.961	0.990	0.975	0.951	0.951	0.951
	k-NN	0.956	0.956	0.044	0.990	0.922	0.990	0.955	0.914	0.912	0.912
Control	Bayesian Network	0.917	0.874	0.083	0.791	0.810	0.944	0.800	0.747	0.754	0.747
	Neural Network	0.882	0.813	0.118	0.714	0.714	0.926	0.714	0.640	0.640	0.640
	Logistic Regression	0.868	0.845	0.132	0.642	0.810	0.883	0.716	0.638	0.692	0.631
	Naive Bayes	0.882	0.854	0.118	0.680	0.810	0.901	0.739	0.668	0.711	0.664
	J48	0.887	0.902	0.113	0.661	0.929	0.877	0.772	0.718	0.805	0.700
	SVM	0.892	0.897	0.108	0.679	0.905	0.889	0.776	0.719	0.794	0.706
	KStar	0.907	0.888	0.093	0.735	0.857	0.920	0.791	0.735	0.777	0.732
	k-NN	0.912	0.891	0.088	0.750	0.857	0.926	0.800	0.746	0.783	0.744
Neurological Control	Bayesian Network	0.902	0.816	0.098	0.778	0.700	0.951	0.737	0.678	0.651	0.677
	Neural Network	0.848	0.738	0.152	0.615	0.600	0.909	0.608	0.513	0.509	0.513
	Logistic Regression	0.848	0.668	0.152	0.655	0.475	0.939	0.551	0.471	0.414	0.462
	Naive Bayes	0.809	0.568	0.191	0.519	0.350	0.921	0.418	0.317	0.271	0.309
	J48	0.819	0.532	0.181	0.571	0.300	0.945	0.393	0.320	0.245	0.299
	SVM	0.843	0.650	0.157	0.643	0.450	0.939	0.529	0.449	0.389	0.439
	KStar	0.853	0.670	0.147	0.679	0.475	0.945	0.559	0.485	0.420	0.474
	k-NN	0.833	0.661	0.167	0.594	0.475	0.921	0.528	0.432	0.396	0.428
Parkinson	Bayesian Network	0.956	0.903	0.044	0.727	0.842	0.968	0.780	0.759	0.810	0.756
	Neural Network	0.941	0.869	0.059	0.652	0.789	0.957	0.714	0.686	0.746	0.682
	Logistic Regression	0.946	0.898	0.054	0.667	0.842	0.957	0.744	0.721	0.799	0.715
	Naive Bayes	0.936	0.840	0.064	0.636	0.737	0.957	0.683	0.650	0.694	0.648
	J48	0.922	0.832	0.078	0.560	0.737	0.941	0.636	0.600	0.677	0.593
	SVM	0.941	0.842	0.059	0.667	0.737	0.962	0.700	0.669	0.699	0.667
	KStar	0.941	0.920	0.059	0.630	0.895	0.946	0.739	0.721	0.841	0.707
	k-NN	0.917	0.857	0.083	0.536	0.789	0.930	0.638	0.607	0.719	0.593