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Table 3 Diagnostic performance in the internal and external validation sets

From: Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea

Validation

Classification algorithm

ROC-AUC (95% CI)

Sensitivity (%, 95% CI)

Specificity (%, 95% CI)

PPV (%, 95% CI)

NPV (%, 95% CI)

P-value

Internal validation

Logistic regression

0.840 (0.818, 0.860)

75.0 (34.9, 96.8)

85.6 (83.5, 87.5)

3.3 (2.1, 4.9)

99.8 (99.3, 99.9)

Reference

Decision tree

0.569 (0.541, 0.597)

100.0 (63.1, 100.0)

13.8 (11.9, 15.9)

0.7 (0.7, 0.8)

100.0 (97.8, 100.0)

< 0.001

Gradient Boosting

0.699 (0.673, 0.725)

87.5 (47.3, 99.6)

58.5 (55.7, 61.2)

1.3 (1.0, 1.7)

99.8 (99.1, 99.9)

0.003

Random Forest

0.763 (0.739, 0.787)

75.0 (34.9, 96.8)

71.5 (68.8, 73.9)

1.7 (1.1, 2.5)

99.7 (99.2, 99.9)

0.049

naïve Bayes

0.847 (0.826, 0.867)

100.0 (63.1, 100.0)

67.4 (64.7, 70.1)

1.9 (1.8, 2.1)

100.0 (99.5, 100.0)

0.837

ANN

0.856 (0.835, 0.875)

75.0 (34.9, 96.8)

84.5 (82.4, 86.5)

3.1 (2.0, 4.6)

99.8 (99.3, 99.9)

0.601

Google Vertex AI

0.842 (0.820, 0.861)

100.0 (63.1, 100.0)

66.7 (63.9, 69.3)

1.9 (1.7, 2.0)

100.0 (99.5, 100.0)

0.848

External validation

Logistic regression

0.738 (0.716, 0.759)

62.5 (24.5, 91.4)

82.9 (81.1, 84.7)

1.7 (0.9, 2.9)

99.7 (99.4, 99.9)

Reference

Decision tree

0.546 (0.522, 0.570)

87.5 (47.3, 99.6)

21.5 (19.5, 23.5)

0.5 (0.4, 0.7)

99.7 (98.3, 99.9)

0.008

Gradient Boosting

0.693 (0.670, 0.715)

87.5 (47.3, 99.6)

48.5 (46.1, 50.9)

0.8 (0.6, 1.0)

99.8 (99.2, 99.9)

0.612

Random Forest

0.746 (0.724, 0.766)

75.0 (34.9, 96.8)

75.6 (73.5, 77.7)

1.4 (0.9, 2.1)

99.8 (00.4, 99.9)

0.898

naïve Bayes

0.760 (0.739, 0.780)

87.5 (47.3, 99.6)

70.5 (68.3, 72.7)

1.4 (1.0, 1.8)

99.9 (99.4, 99.9)

0.765

ANN

0.784 (0.763, 0.803)

62.5 (24.5, 91.4)

93.7 (92.4, 94.8)

4.5 (2.6, 7.7)

99.8 (99.5, 99.9)

0.044

Google Vertex AI

0.761 (0.740, 0.781)

87.5 (47.3, 99.6)

70.7 (68.5, 72.9)

1.4 (1.0, 1.8)

99.9 (99.4–99.9)

0.756

  1. ANN, artificial neural networks; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value; ROC-AUC, the areas under the receiver operating characteristic curve