Table 2:

Diagnostic performance of logistic regression, the linear Support Vector Machine, and the deep learning–based automatic classification algorithm in the datasets^{^a}

	Logistic Regression	Linear SVM	XGBoost	P Value^{^b}	P Value^{^c}
AD vs MCI
Asan Medical Center	0.770 (0.761–0.779)	0.772 (0.761–0.782)	0.803 (0.802–0.805)	<.001	<.001
Kyung Hee University Hospital at Gangdong	0.798 (0.775–0.822)	0.804 (0.783–0.824)	0.825 (0.810–0.840)	.018	.030
ADNI	0.706 (0.702–0.710)	0.700 (0.695–0.704)	0.758 (0.755–0.760)	<.001	<.001
MCI vs healthy control
Asan Medical Center	0.812 (0.806–0.817)	0.830 (0.821–0.840)	0.870 (0.868–0.872)	<.001	<.001
Kyung Hee University Hospital at Gangdong	0.692 (0.678–0.706)	0.687 (0.669–0.706)	0.705 (0.699–0.712)	.029	.023
ADNI	0.698 (0.686–0.710)	0.702 (0.697–0.708)	0.668 (0.664–0.671)	<.001	<.001
AD vs healthy controls
Asan Medical Center	0.953 (0.949–0.958)	0.960 (0.958–0.963)	0.982 (0.980–0.985)	<.001	<.001
Kyung Hee University Hospital at Gangdong	0.905 (0.889–0.921)	0.911 (0.903–0.920)	0.940 (0.933–0.947)	<.001	<.001
ADNI	0.863 (0.856–0.870)	0.860 (0.857–0.863)	0.885 (0.879–0.891)	<.001	<.001
OASIS^{^d}	0.826 (0.817–0.835)	0.820 (0.809–0.832)	0.840 (0.837–0.844)	.001	<.001

↵a Data are AUC (95% CI).
↵b P values: between logistic regression and XGBoost.
↵c P values: between linear SVM and XGBosst.
↵d OASIS dataset included only AD and healthy controls.