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Table 3 Confusion matrix and AUC values of each model on the test set

From: Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning

Random sampling

Acc (%)

Sen (%)

Spe (%)

PPV (%)

NPV (%)

AUC (%)

 LR

80.95

75.00

85.25

78.57

82.54

89.40

 DT

74.29

61.36

83.61

72.97

75.00

84.30

 SVM

85.71

81.82

88.52

83.72

87.10

90.50

 XGBoost

87.62

86.36

88.52

84.44

90.00

92.20

tenfold cross-validation

 LR

79.61

78.53

80.24

68.38

87.98

87.98

 DT

78.75

77.69

79.33

68.15

87.10

78.38

 SVM

76.13

70.83

78.95

69.42

81.51

87.27

 XGBoost

83.43

82.83

83.83

72.12

90.51

89.45

  1. AUC area under the curve, Acc accuracy, Sen sensitivity, Spe specificity, PPV positive predictive value, NPV negative predictive value, LR logistic regression, DT decision tree, SVM support vector machine, XGBoost eXtreme Gradient Boosting
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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