|
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
