Table 2.

Top 10 Models Selected for Each Outcome.

Classifier	Feature Selection	Accuracy	Sensitivity	AUC±std	Specificity±std
Table 2a. Overall MCI
LR	Fisher	0.627	0.793	0.764±0.01	0.599±0.01
SVM	Fisher	0.710	0.676	0.764±0.01	0.716±0.02
LR	Gini	0.628	0.789	0.764±0.01	0.601±0.01
SVM	Gini	0.710	0.676	0.763±0.01	0.716±0.02
LR	mRMR	0.627	0.789	0.763±0.01	0.600±0.01
LR	Chi2	0.625	0.790	0.763±0.01	0.598±0.01
SVM	mRMR	0.710	0.677	0.762±0.01	0.717±0.02
SVM	Chi2	0.709	0.679	0.762±0.01	0.715±0.01
LR	InfoGain	0.625	0.789	0.762±0.01	0.598±0.01
SVM	InfoGain	0.709	0.678	0.761±0.01	0.715±0.01
LR	LASSO	0.689	0.700	0.765±0.01	0.687±0.02
Table 2b. aMCI
SVM	Fisher	0.710	0.711	0.774±0.02	0.710±0.01
SVM	Chi2	0.709	0.715	0.773±0.02	0.709±0.01
LR	Fisher	0.595	0.832	0.773±0.02	0.574±0.02
SVM	Gini	0.709	0.714	0.773±0.02	0.708±0.01
SVM	InfoGain	0.710	0.714	0.773±0.02	0.709±0.01
SVM	mRMR	0.709	0.715	0.773±0.02	0.708±0.01
LR	Gini	0.594	0.832	0.772±0.01	0.575±0.02
LR	Chi2	0.594	0.829	0.772±0.01	0.574±0.02
LR	mRMR	0.595	0.832	0.772±0.01	0.574±0.02
LR	InfoGain	0.594	0.829	0.772±0.01	0.574±0.02
LR	LASSO	0.666	0.752	0.773±0.02	0.654±0.01
Table 2c. naMCI
RF	Chi2	0.692	0.658	0.732±0.03	0.710±0.03
RF	Gini	0.691	0.656	0.730±0.03	0.706±0.03
RF	InfoGain	0.692	0.655	0.730±0.03	0.709±0.03
RF	mRMR	0.693	0.652	0.728±0.03	0.712±0.03
LR	Chi2	0.590	0.734	0.728±0.03	0.605±0.03
SVM	Gini	0.745	0.579	0.727±0.03	0.752±0.03
SVM	Chi2	0.744	0.574	0.727±0.03	0.752±0.03
LR	Gini	0.592	0.734	0.727±0.03	0.607±0.03
SVM	InfoGain	0.745	0.573	0.726±0.03	0.753±0.03
LR	InfoGain	0.593	0.727	0.725±0.03	0.608±0.03
LR	LASSO	0.622	0.704	0.722±0.03	0.619±0.02

The best model (high AUC with the highest specificity) is marked bold fonts. For AUC and specificity we report the standard deviation over the 100 trials. The 95% confidence interval can be obtained by the confidence interval formula on page 7.