Table 2.

Performance summary of machine learning models in CMP-IV group.

Model	Accuracy (95% CI)	Precision (95% CI)	Recall (95% CI)	F1 (95% CI)	AUC (95% CI)
XGBoost	0.802 (0.760, 0.844)	0.799 (0.782, 0.816)	0.633 (0.551, 0.772)	0.706 (0.664, 0.748)	0.768 (0.742, 0.786)
LGBM	0.804 (0.746, 0.836)	0.769 (0.796, 0.742)	0.582 (0.438, 0.732)	0.659 (0.575, 0.746)	0.766 (0.744, 0.792)
CatBoost	0.807 (0.729, 0.861)	0.764 (0.716, 0.812)	0.631 (0.532, 0.778)	0.697 (0.647, 0.747)	0.762 (0.723, 0.801)
RF	0.788 (0.769, 0.802)	0.736 (0.708, 0.764)	0.612 (0.598, 0.621)	0.671 (0.656, 0.687)	0.732 (0.701, 0.763)
GBDT	0.793 (0.785, 0.801)	0.717 (0.632, 0.802)	0.534 (0.503, 0.579)	0.611 (0.566, 0.655)	0.702 (0.665, 0.739)
Bagging	0.796 (0.744, 0.859)	0.716 (0.688, 0.744)	0.522 (0.387, 0.591)	0.615 (0.575, 0.656)	0.698 (0.652, 0.744)
LR	0.777 (0.732, 0.811)	0.689 (0.505, 0.819)	0.494 (0.405, 0.619)	0.588 (0.450, 0.702)	0.688 (0.664, 0.712)
SVM	0.795 (0.758, 0.836)	0.612 (0.568, 0.656)	0.512 (0.459, 0.565)	0.561 (0.512, 0.609)	0.687 (0.646, 0.728)
AdaBoost	0.773 (0.702, 0.812)	0.698 (0.601, 0.795)	0.533 (0.501, 0.565)	0.608 (0.552, 0.665)	0.668 (0.624, 0.712)
MLP	0.744 (0.688, 0.805)	0.637 (0.459, 0.781)	0.515 (0.488, 0.542)	0.575 (0.488, 0.656)	0.658 (0.616, 0.691)

XGBoost, extreme gradient boosting; LGBM, light gradient boosting machine; CatBoost, category boosting; RF, random forest; GBDT, Gradient boosting decision tree; Bagging, bootstrap aggregation; LR, logistic regression; SVM, support vector machine; AdaBoost, adaptive boosting; MLP, multi-layer perceptron; AUC, the area under the receiver operating characteristic curve.