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Table 3 The evaluation metrics with 95% confidence intervals for each model using 20-round fivefold cross-validation

From: Machine learning-based risk models for procedural complications of radiofrequency ablation for atrial fibrillation

outcomes

Model

AUCa (95% CI)

Accuracy (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

F score (95% CI)

Brier score (95% CI)

Any complications

LRb

0.650(0.645,0.655)

0.858(0.855,0.860)

0.352(0.339,0.366)

0.867(0.865,0.870)

0.084(0.081,0.087)

0.142(0.140,0.145)

DTc

0.627(0.613,0.641)

0.599(0.584,0.615)

0.615(0.589,0.642)

0.599(0.584,0.615)

0.054(0.051,0.056)

0.401(0.385,0.416)

RFd

0.721(0.713,0.729)

0.834(0.832,0.836)

0.460(0.446,0.475)

0.841(0.838,0.843)

0.092(0.090,0.095)

0.166(0.164,0.168)

GBMe

0.688(0.679,0.697)

0.929(0.927,0.930)

0.239(0.225,0.252)

0.942(0.940,0.943)

0.110(0.103,0.116)

0.071(0.070,0.073)

XGBoostf

0.707(0.701,0.712)

0.899(0.897,0.901)

0.327(0.315,0.340)

0.910(0.908,0.912)

0.107(0.103,0.111)

0.101(0.099,0.103)

Cardiac effusion/tamponade

LR

0.665(0.656,0.674)

0.921(0.918,0.923)

0.259(0.241,0.277)

0.926(0.924,0.929)

0.052(0.048,0.055)

0.079(0.077,0.082)

DT

0.606(0.589,0.623)

0.429(0.398,0.459)

0.711(0.673,0.749)

0.426(0.396,0.457)

0.020(0.019,0.021)

0.571(0.541,0.602)

RF

0.662(0.647,0.677)

0.918(0.917,0.919)

0.295(0.281,0.308)

0.923(0.922,0.924)

0.056(0.054,0.059)

0.082(0.081,0.083)

GBM

0.660(0.644,0.675)

0.945(0.944,0.946)

0.195(0.166,0.223)

0.951(0.950,0.952)

0.055(0.047,0.063)

0.055(0.054,0.056)

XGBoost

0.696(0.688,0.703)

0.681(0.671,0.692)

0.652(0.632,0.672)

0.682(0.671,0.692)

0.033(0.032,0.034)

0.319(0.308,0.329)

Hemorrhage/hematoma

LR

0.745(0.737,0.752)

0.938(0.936,0.939)

0.207(0.190,0.225)

0.944(0.942,0.945)

0.051(0.046,0.055)

0.062(0.061,0.064)

DT

0.649(0.630,0.668)

0.807(0.799,0.814)

0.470(0.429,0.512)

0.809(0.801,0.817)

0.037(0.035,0.040)

0.193(0.186,0.201)

RF

0.839(0.832,0.845)

0.903(0.902,0.904)

0.463(0.440,0.486)

0.906(0.905,0.908)

0.071(0.067,0.075)

0.097(0.096,0.098)

GBM

0.780(0.766,0.795)

0.985(0.985,0.986)

0.161(0.151,0.171)

0.992(0.991,0.992)

0.148(0.140,0.157)

0.015(0.014,0.015)

XGBoost

0.794(0.783,0.806)

0.860(0.857,0.862)

0.450(0.428,0.472)

0.863(0.861,0.866)

0.049(0.047,0.051)

0.140(0.138,0.143)

  1. aAUC Area under the ROC curve
  2. bLR Logistic regression
  3. cDT Decision tree
  4. dRF Random forest
  5. eGBM Gradient boosting machine
  6. fXGBoost Extreme gradient boosting