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Table 2 Performance comparison between univariate model and XGBoost model

From: Machine learning-based prediction model and visual interpretation for prostate cancer

Model

Accuracy(%)

Sensitivity(%)

Specificity(%)

AUC (95% CI)

XGBoost

74.09

79.57

71.12

0.82 (0.79–0.82)

f/tPSA

71.82

75.46

67.07

0.75 (0.72–0.76)*

tPSA

64.70

68.33

59.40

0.68 (0.65–0.70)*

fPSA

57.04

60.36

50.00

0.61 (0.58–0.63)*

  1. * The AUC of XGBoost was significantly compared with that of f/tPSA, tPSA and fPSA models (each P<0.001)
  2. f/tPSA free-to-total PSA ratio, tPSA total prostate-specific antigen, fPSA free prostate-specific antigen
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BMC Urology

ISSN: 1471-2490

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