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Table 5 Classification performance in the larger graph dataset (mean \(\pm\) std)

From: GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification

Model

Accuracy

Sensitivity

Specificity

F1

AUC

MCC

SVM

0.9242 ± 0.0114

0.9235 ± 0.0133

0.9249 ± 0.0129

0.9247 ± 0.0113

0.9242 ± 0.0114

0.8485 ± 0.0228

RF

0.5975 ± 0.0127

0.6367 ± 0.0210

0.5541 ± 0.0094

0.6133 ± 0.0151

0.5954 ± 0.0127

0.1916 ± 0.0257

CNN

0.5917 ± 0.0172

0.6714 ± 0.0383

0.5160 ± 0.0394

0.5559 ± 0.1888

0.5911 ± 0.0017

0.3018 ± 0.0259

GAT2

0.9518 ± 0.0121

0.9568 ± 0.0344

0.9466 ± 0.0059

0.9526 ± 0.0099

0.9517 ± 0.0123

0.9978 ± 0.0006

  1. The bold means it is the best result for each metric (column of the table)