Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients

Table 3 Evaluation of machine learning models after 10-fold cross-validation

Model name	Accuracy	Precision	Recall	F1 score	AUC
LR	0.650	0.643	0.668	0.655	0.650
DT	0.606	0.616	0.552	0.583	0.605
RF	0.619	0.618	0.613	0.616	0.619
SVM	0.664	0.656	0.659	0.658	0.712
NB	0.644	0.657	0.595	0.624	0.643
KNN	0.617	0.611	0.639	0.625	0.617
XGB	0.630	0.632	0.616	0.624	0.630
ANN	0.606	0.596	0.645	0.619	0.606

AUC: Area under curve; LR: Logistic regression; DT: Decision tree; RF: Random forest; SVM: Support vector machine; NB: Naïve bayes; KNN: K-nearest Neighbour; XGB: eXtreme Gradient Boosting; ANN: Artificial neural network.