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Table 6 comparing the performance of different classifiers for stillbirth prediction in this study

From: Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study

Feature Set

Classifier

Accuracy

Precision

Recall (Sensitivity)

Specificity

F-Score

AUC

FSC22

DT

78.31

66.81

86.79

69.03

75.50

78.31

LR

76.57

70.38

80.34

70.25

75.03

76.60

SVM

78.62

65.55

88.76

65.33

75.42

78.69

GBC

78.54

67.17

86.95

67.18

75.79

78.65

RF

78.67

67.97

86.49

68.00

76.12

78.67

SE

79.60

70.85

87.19

71.12

80.03

79.82

FSC43

DT

75.27

71.27

77.50

70.95

74.26

75.24

LR

80.90

74.75

85.24

74.72

79.66

80.93

SVM

80.68

71.19

87.88

70.90

78.65

80.59

GBC

82.02

77.52

85.20

77.48

81.17

82.12

RF

81.88

75.47

86.59

75.61

80.65

81.91

SE

89.93

87.10

91.18

87.04

89.90

89.77

FSC67

DT

75.41

76.00

75.13

76.16

75.56

75.32

LR

73.54

69.76

75.48

69.69

72.51

73.49

SVM

74.66

83.78

73.96

83.81

78.46

74.35

GBC

82.37

79.03

84.70

78.84

81.77

82.26

RF

82.30

75.7

87.24

75.74

81.13

82.17

SE

90.56

88.02

91.37

88.10

90.58

90.00

FFS

DT

73.93

74.90

72.77

74.67

73.82

73.97

LR

81.74

74.02

86.82

74.11

79.91

81.60

SVM

68.52

75.10

65.67

74.97

70.07

68.64

GBC

82.89

76.17

87.34

76.19

81.38

82.77

RF

80.49

70.02

87.76

70.13

77.89

80.31

SE

90.55

87.14

92.19

87.06

90.55

89.85