Table 2 Model performance on test set data for predicting 30-day major adverse cardiovascular events following open abdominal aortic aneurysm repair using pre-operative features.
From: Predicting outcomes following open abdominal aortic aneurysm repair using machine learning
AUROC (95% CI) | Accuracy (95% CI) | Sensitivity | Specificity | PPV | NPV | |
|---|---|---|---|---|---|---|
XGBoost | 0.90 (0.89–0.91) | 0.81 (0.80–0.82) | 0.81 | 0.81 | 0.82 | 0.80 |
Random forest | 0.88 (0.87–0.89) | 0.80 (0.78–0.81) | 0.83 | 0.77 | 0.76 | 0.84 |
RBF SVM | 0.86 (0.85–0.88) | 0.78 (0.76–0.79) | 0.77 | 0.79 | 0.81 | 0.75 |
Naïve Bayes | 0.82 (0.81–0.83) | 0.82 (0.81–0.84) | 0.80 | 0.84 | 0.86 | 0.78 |
MLP ANN | 0.77 (0.75–0.79) | 0.72 (0.70–0.73) | 0.70 | 0.75 | 0.83 | 0.60 |
Logistic regression | 0.66 (0.64–0.68) | 0.58 (0.56–0.60) | 0.55 | 0.71 | 0.70 | 0.55 |
