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Table 2 Performance measures of data mining algorithm at different levels of significance over Type 1 diabetes dataset

From: Comparative study of classification algorithms for immunosignaturing data

SIGNIFICANCE

p < 5 x 10-13

p < 5 x 10-10

p < 5 x 10-7

p < 5 x 10-4

 

Algorithm

Acc.

Sp

Sn

AUC

Acc.

Sp

Sn

AUC

Acc.

Sp

Sn

AUC

Acc

Sp

Sn

AUC

Avg.

SLR

87.5

85.0

89.7

0.93

92.5

90.2

94.9

0.97

92.5

92.0

92.0

0.96

92.5

90.0

94.9

0.96

92.2

Naïve Bayes

90.0

85.4

95.0

0.97

91.3

90.2

92.3

0.98

92.5

90.2

95.0

0.96

89.0

85.4

92.3

0.92

92.0

SVM

88.8

82.9

94.9

0.89

90.0

82.9

97.4

0.90

93.8

90.2

97.4

0.93

93.8

92.7

94.9

0.94

91.6

R. Forest

87.5

87.8

87.2

0.96

92.5

90.2

94.9

0.97

91.5

87.8

94.9

0.97

88.8

85.4

92.3

0.94

91.5

KNN

92.5

90.2

94.9

0.95

95.0

92.7

97.4

0.96

90.0

85.4

94.9

0.93

85.0

80.5

89.7

0.90

91.4

Logistic. R

86.3

87.8

84.6

0.82

92.5

90.2

94.9

0.97

92.5

92.7

97.4

0.97

87.5

92.7

82.1

0.92

90.6

VFI

87.5

82.9

92.3

0.95

92.5

90.2

94.9

0.97

88.8

85.4

92.3

0.95

87.5

82.9

92.3

0.92

90.5

Bayes Net

91.3

90.2

92.3

0.97

90.0

85.4

94.9

0.98

90.0

85.4

94.9

0.95

83.8

78.0

89.7

0.89

90.3

MLP

80.0

80.5

79.5

0.89

91.3

90.2

92.3

0.98

93.8

90.2

97.4

0.99

dnf

dnf

dnf

dnf

90.1*

Hyper Pipes

87.5

90.2

84.6

0.96

91.3

90.2

92.3

0.97

90.0

90.2

89.7

0.95

83.8

92.7

74.4

0.92

89.8

K-means

91.3

82.9

100

0.92

90.0

82.9

97.4

0.90

86.3

78.0

94.9

0.87

85.0

75.6

94.9

0.85

88.3

M5P

88.8

85.4

92.3

0.94

85.0

80.5

89.7

0.94

81.3

78.0

84.6

0.87

78.8

73.2

84.6

0.85

85.1

Random Tree

85.0

87.8

82.1

0.85

78.8

75.6

82.1

0.79

87.5

85.4

89.7

0.88

83.8

85.4

82.1

0.84

83.8

K star

87.5

87.8

87.2

0.96

91.3

85.4

97.4

0.98

90.0

85.4

94.9

0.97

53.8

100

5.1

0.54

81.9

J48

86.3

85.4

87.2

0.79

81.3

82.9

79.5

0.83

78.8

82.9

74.4

0.72

80.0

85.4

74.4

0.73

80.3

ASC

86.3

85.4

87.2

0.79

80.0

82.9

76.9

0.80

80.0

87.8

71.8

0.78

66.3

80.5

51.3

0.55

76.8

LDA

88.8

82.9

94.9

0.96

91.3

85.4

97.4

0.95

40.0

96.7

15.8

0.68

21.3

94.4

0.0

0.48

69.7

  1. Acc: Accuracy, Sp: Specificity, Sn: Sensitivity, AUC: Area under ROC curve, Avg: Average score in % for each algorithms, dnf: “Did Not Finish”, * denotes Avg. from 3 significance levels. Measures >90% are marked in bold.