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Table 10 Best models with accuracy, AUC, classification method and selected features

From: Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

 

Accuracy

AUC

Classification method

Selected features

Group 1

    

CC-3-input

74.76

0.70

ANFIS

Age,Inv,PN

GA-3-input

70.95

0.66

ANFIS

PT,PN,Sta

CC-GA-6-input

70.95

0.73

LR

Gen,Dri,Node,PT,PN,Sta

Group 2

    

ReliefF-GA-3-input

93.81

0.90

ANFIS

Dri,Inv,p63

ReliefF-GA-4-input

93.81

0.90

ANFIS

Dri,Inv,Tre,p63

ReliefF-GA-3-input

84.62

0.83

ANN

Dri,Inv,p63

ReliefF-GA-3-input

84.62

0.83

ANN

Dri,Inv,p63

GA-3-input

74.76

0.74

ANFIS

Inv,Node,p63

CC-GA-3-input

74.76

0.70

ANFIS

Inv,Node,p63

CC-GA-3-input

74.76

0.70

SVM

Inv,Node,p63

CC-GA-3-input

74.76

0.70

LR

Inv,Node,p63

ReliefF-GA-3-input

74.76

0.70

SVM

Dri,Inv,p63

ReliefF-GA-3-input

74.76

0.70

LR

Dri,Inv,p63

Relief-GA-4-input

74.76

0.70

LR

Dri,Inv,Tre,p63

Relief-GA-5-input

74.76

0.70

SVM

Age,Gen,Smo,Dri,p63

Relief-GA-6-input

74.43

0.66

SVM

Age,Gen,Smo,Dri,Inv,p63

Relief-GA-4-input

73.38

0.75

ANN

Dri,Inv,Tre,p63

Relief-GA-4-input

71.43

0.68

SVM

Dri,Inv,Tre,p63

Relief-GA-5-input

71.43

0.68

LR

Age,Gen,Smo,Dri,p63

CC-3-input

71.43

0.67

LR

Inv,PN,p63

CC-4-input

71.43

0.67

LR

Age,Inv,PN,p63

CC-GA-4-input

70.48

0.71

ANFIS

Gen,Inv,Size,p53