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Table 20 From Yi et al. the proposed method is CatBoost

From: CatBoost for big data: an interdisciplinary review

Method

Accuracy (%)

Sensitivity (%)

Specificity (%)

MAN-HOPE-LR

83.75 ± 0.11

83.21 ± 0.47

84.30 ± 0.32

MAN-HOPE-Ada

84.73 ± 0.18

85.53 ± 0.29

83.93 ± 0.22

MAN-HOPE-RF

92.66 ± 0.12

92.03 ± 0.15

93.29 ± 0.22

MAN-HOPE-XGB

89.56 ± 0.41

90.60 ± 0.28

88.51 ± 0.95

Proposed method

93.30 ± 0.12

91.50 ± 0.14

95.10 ± 0.11

Method

Precision (%)

MCC (%)

AUC (%)

MAN-HOPE-LR

84.13 ± 0.20

67.52 ± 0.22

91.58 ± 0.13

MAN-HOPE-Ada

84.19 ± 0.18

69.48 ± 0.36

92.07 ± 0.13

MAN-HOPE-RF

93.21 ± 0.20

85.33 ± 0.24

97.12 ± 0.05

MAN-HOPE-XGB

88.75 ± 0.81

79.13 ± 0.79

96.02 ± 0.24

Proposed method

94.91 ± 0.11

86.66 ± 0.24

97.93 ± 0.08

  1. Best metrics are highlighted in italic; we split table in two for legibility