Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology

Table 3 Statistical measures for all models

Model name	Area under curve	Accuracy	Precision	Recall	Specificity	F1 score	Positive predict value	Positive predict value
Random forest	0.97 (0.94-1.00)	0.93 (0.87-0.98)	0.92 (0.85-1.00)	0.87 (0.72-1.00)	0.95 (0.92-1.00)	0.88 (0.80-0.98)	0.92 (0.85-1.00)	0.91 (0.84-1.00)
AdaBoost	0.94 (0.88-0.99)	0.84 (0.76-0.94)	0.85 (0.69-1.00)	0.71 (0.53-0.90)	0.91 (0.84-1.00)	0.76 (0.62-0.91)	0.88 (0.69-1.00)	0.85 (0.74-0.95)
Artificial neural network	0.77 (0.61-0.85)	0.63 (0.52-0.74)	0.51 (0.30-0.70)	0.49 (0.31-0.73)	0.69 (0.55-0.83)	0.47 (0.32-0.67)	0.46 (0.30-0.70)	0.73 (0.56-0.85)
Decision tree	0.79 (0.67-0.89)	0.81 (0.69-0.90)	0.74 (0.56-0.94)	0.69 (0.50-0.86)	0.85 (0.75-0.97)	0.71 (0.55-0.86)	0.74 (0.56-0.94)	0.84 (0.71-0.93)
Extra tree	0.97 (0.95-1.00)	0.94 (0.87-0.98)	0.91 (0.83-1.00)	0.85 (0.71-1.00)	0.91 (0.91-1.00)	0.88 (0.80-0.98)	0.93 (0.83-1.00)	0.92 (0.84-1.00)
Gradient boosting machine	0.92 (0.84-0.98)	0.86 (0.75-0.94)	0.83 (0.67-1.00)	0.72 (0.55-0.92)	0.91 (0.83-1.00)	0.76 (0.64-0.91)	0.84 (0.67-1.00)	0.84 (0.75-0.96)
K-nearest neighbors	0.89 (0.80-0.96)	0.82 (0.71-0.90)	0.73 (0.54-0.90)	0.72 (0.55-0.91)	0.83 (0.72-0.95)	0.68 (0.57-0.86)	0.73 (0.54-0.90)	0.85 (0.72-0.95)
Lightgbm	0.96 (0.91-0.99)	0.86 (0.79-0.95)	0.81 (0.73-1.00)	0.74 (0.54-0.92)	0.94 (0.87-1.00)	0.79 (0.65-0.92)	0.85 (0.74-1.00)	0.86 (0.76-0.95)
Logistic regression	0.73 (0.60-0.85)	0.67 (0.55-0.77)	0.54 (0.33-0.74)	0.61 (0.41-0.82)	0.68 (0.53-0.83)	0.58 (0.38-0.73)	0.58 (0.33-0.74)	0.76 (0.61-0.89)
Support vector machine	0.74 (0.62-0.86)	0.61 (0.50-0.73)	0.47 (0.23-0.72)	0.34 (0.15-0.56)	0.77 (0.64-0.90)	0.39 (0.19-0.58)	0.47 (0.23-0.72)	0.63 (0.52-0.80)
Extreme gradient boosting	0.96 (0.92-0.99)	0.86 (0.81-0.97)	0.87 (0.74-1.00)	0.78 (0.62-0.95)	0.94 (0.86-1.00)	0.83 (0.70-0.94)	0.84 (0.74-1.00)	0.88 (0.78-0.98)

AdaBoost: Adaptive boosting.