Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection

Table 3 Comparison of the performance of different models in training set, validation set and prospective set

Model		AUC	Sensitivity	Specificity	Accuracy	Precision	F1 score
Training set	LR	0.803	0.733	0.728	0.731	0.774	0.753
	DT	0.754	0.806	0.613	0.721	0.726	0.764
	RF	0.861	0.727	0.835	0.775	0.849	0.784
	SVM	0.808	0.720	0.753	0.734	0.788	0.752
	XGB	0.909	0.756	0.904	0.820	0.907	0.824
Validation set	LR	0.809	0.743	0.686	0.719	0.756	0.750
	DT	0.799	0.785	0.723	0.758	0.788	0.786
	RF	0.902	0.750	0.918	0.823	0.923	0.828
	SVM	0.819	0.743	0.705	0.726	0.767	0.755
	XGB	0.921	0.788	0.914	0.843	0.923	0.850
Prospective set	LR	0.779	0.568	0.847	0.711	0.780	0.657
	DT	0.812	0.765	0.729	0.747	0.765	0.747
	RF	0.943	0.667	0.988	0.831	0.982	0.794
	SVM	0.791	0.617	0.824	0.723	0.769	0.685
	XGB	0.963	0.840	0.941	0.892	0.932	0.883

LR: Logistic regression; DT: Decision Tree; RF: Random forest; SVM: Support vector machine; XGB: Extreme Gradient Boosting; AUC: Area under the curve.