BMC Medical Informatics and Decision Making

Table 4 Machine learning prediction performance

From: In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study

	RLR	SVM	NB	KNN	RF	XGB
AUROC	0.77 (0.69–0.85)	0.80 (0.73–0.87)	0.76 (0.68–0.84)	0.77 (0.69–0.85)	0.83 (0.76–0.90)	0.85 (0.78–0.92)
F1 score	0.42	0.41	0.44	0.41	0.34	0.44
Sensitivity	0.69 (0.56–0.80)	0.39 (0.26–0.52)	0.50 (0.37–0.63)	0.71 (0.58–0.82)	0.21 (0.12–0.34)	0.33 (0.22–0.46)
Specificity	0.73 (0.68–0.78)	0.92 (0.88–0.94)	0.87 (0.82–0.90)	0.71 (0.65–0.76)	0.99 (0.98–1.00)	0.97 (0.95–0.98)
PPV	0.31 (0.23–0.39)	0.44 (0.30–0.58)	0.39 (0.28–0.51)	0.29 (0.22–0.38)	0.85 (0.58–0.96)	0.65 (0.46–0.81)
NPV	0.93 (0.90–0.96)	0.90 (0.86–0.93)	0.91 (0.87–0.94)	0.94 (0.90–0.96)	0.88 (0.84–0.91)	0.90 (0.86–0.92)

AUROC, area under the receiver operating curve; KNN, k-nearest neighbors; NB, naïve Bayes; NPV, negative predictive value; PPV, positive predictive value; RF, random forest; RLR, regularized logistic regression; SVM, support vector machine; XGB, extreme-gradient boosting

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