Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study
Table 2
Comparison of validation results of machine learning models.
Models
Accuracy
AUC
Recall
Precision
F1 value
CatBoost
0.89
0.87
0.33
0.78
0.44
RF
0.89
0.88
0.26
0.82
0.38
XGBoost
0.90
0.83
0.41
0.81
0.51
LR
0.89
0.82
0.38
0.63
0.46
KNN
0.88
0.75
0.21
0.61
0.31
Model with oversampling (SMOTEENN)
CatBoost
0.96
0.99
0.98
0.95
0.97
RF
0.95
0.99
0.98
0.94
0.96
XGBoost
0.94
0.98
0.98
0.92
0.95
LR
0.91
0.95
0.92
0.92
0.92
KNN
0.92
0.96
0.98
0.88
0.93
Tradition risk score model
GRACE score
0.84
0.80
0.46
0.59
0.51
AUC and F1 score: the higher, the better. XGBoost: Extreme Gradient Boosting; RF: random forest; LR: logistic regression; KNN: -nearest neighbors.
