Table 2.

Testing results of the three knowledge incorporation models in comparison with other risk scores and machine learning methods

Model	AUC	Sensitivity	Specificity	F-score	G-mean
(1) Pre-mode	0.77	0.83	0.57	0.17	0.69
(2) In-mode	0.80	0.70	0.80	0.26	0.75
(3) Post-mode	0.82	0.60	0.88	0.32	0.73
Mehran’s (> 7.8)	0.70	0.24	0.94	0.20	0.47
Chen's (≥ 13)	0.72	0.42	0.88	0.24	0.61
Gao's (> 5)	0.67	0.34	0.94	0.29	0.57
AGEF (≥ 0.66)	0.62	0.37	0.88	0.21	0.57
Logistic regression	0.59	0.84	0.33	0.12	0.53
Decision tree	0.58	0.61	0.55	0.12	0.58
Random forest	0.64	0.58	0.72	0.17	0.64
Easy ensemble	0.70	0.61	0.79	0.23	0.69

The evaluation metrics are defined as follows:

Specificity = TN/(TN + FP); Sensitivity = TP/(TP + FN); Precision = TP/(TP + FP); F-score = 2*Precision*Recall/(Precision + Recall) if TP > 0 and 0 if TP = 0; TP is the count of true positives, FP of false positive, TN of true negatives and FN of false negatives

AUC, areas-under-curve; AGEF, Age, Glomerular filtration rate and Ejection Fraction