Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery

Table 2 Receiver operating characteristic curve analyses for predicting urinary tract infection in each machine learning-based model

Model	Training set			Testing set
	AUC mean	AUC 95%CI	Variables1	AUC mean	AUC 95%CI	Variables1
RFC	0.925	0.868-0.982	7	0.918	0.861-0.975	7
SVM	0.787	0.730-0.844	7	0.779	0.722-0.836	7
DT	0.776	0.719-0.833	7	0.769	0.712-0.826	7
ANN	0.879	0.822-0.936	6	0.854	0.797-0.911	6
XGboost	0.797	0.740-0.854	7	0.788	0.731-0.845	7

¹Variables included in the model. ANN: Artificial neutral network; AUC: Area under the curve; CI: Confidence interval; DT: Decision tree; RFC: Random forest classifier; SVM: Support vector machine; XGboost: Extreme gradient boosting.