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Table 4 Best predictive performance results in fivefold cross-validation of classifiers trained on the simplified set and the full set of features

From: Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes

 

Sensitivity [%] (recall)

Specificity [%]

Accuracy [%]

G-mean [%]

AUC

 

Logistic regression

78 ± 25

30 ± 31

65 ± 10

48.4a

54 ± 3

Significant lesion

Xgboost

56 ± 18

58 ± 20

57 ± 8

57.0a

57 ± 2

DRSA-BRE (full set of features)

56.9 ± 0.2

66.9 ± 0.2

59.6 ± 0.2

61.7 ± 0.02

61.9a

Logistic regression

47 ± 34

90 ± 11

89 ± 10

65.0a

68 ± 11

In-hospital death

Xgboost

80 ± 9

79 ± 4

80 ± 4

79.5a

78 ± 3

DRSA-BRE

79.3 ± 1.7

80.6 ± 0.5

81.0 ± 0.5

79.9 ± 1

80.8a

DRSA-BRE (full set of features)

81.0 ± 2.4

81.1 ± 0.5

81.0 ± 0.5

81.0 ± 1

81.0a

  1. aIndicates that value was not directly estimated during experiments