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

Table 2 Evaluation of machine learning models in the original data

Model name	Accuracy	Precision	Recall	F1 score	AUC
LR	0.680	0.680	0.676	0.678	0.747
DT	0.924	0.994	0.853	0.918	0.988
RF	0.924	0.940	0.905	0.922	0.985
SVM	0.651	0.636	0.703	0.667	0.739
NB	0.657	0.676	0.598	0.634	0.709
KNN	0.747	0.748	0.742	0.745	0.828
XGB	0.912	0.941	0.879	0.901	0.976
ANN	0.886	0.899	0.868	0.883	0.963

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.