Table 9 Comparative evaluation of the DE algorithm versus metaheuristic algorithms using the ADNI dataset

From: Automated Alzheimer’s disease detection using active learning model with reinforcement learning and scope loss function

Algorithm

Accuracy

F-measure

G-measure

AUC

FA

78.606 ± 0.070

82.980 ± 0.057

83.635 ± 0.014

0.718 ± 0.043

SSA

79.552 ± 0.072

84.424 ± 0.013

85.087 ± 0.037

0.735 ± 0.012

BA

80.520 ± 0.008

85.332 ± 0.100

86.026 ± 0.042

0.751 ± 0.002

COA

82.186 ± 0.089

86.507 ± 0.060

87.245 ± 0.067

0.761 ± 0.086

HMS

83.435 ± 0.077

87.473 ± 0.042

88.272 ± 0.006

0.774 ± 0.083

ABC

84.768 ± 0.086

88.712 ± 0.053

89.445 ± 0.072

0.784 ± 0.065

DE

86.367 ± 0.051

89.520 ± 0.077

90.232 ± 0.054

0.800 ± 0.028

Proposed DE

90.623 ± 0.025

93.685 ± 0.073

93.165 ± 0.068

0.810 ± 0.093

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