Table 2.

The basic characteristics of the risk prediction model

Study	Data set partitioning	Missing value handling method	Efficiency of model		Verification of model		Model presentation
			Distinction	Calibration degree	Internal verification	External verification
Ruan ZB et al. [13]	NA	NA	AUC, Sensitivity, Specificity	Calibration curve	Bootstrap	NA	Nomograph
Budzianowski J et al. [14]	Cross verification	NA	AUC	NA	NA	NA	SHAP
Zheng D et al. [15]	7:3	NA	AUC	Calibration curve, Decision curve	Bootstrap	Yes	Nomograph
Zhao Z et al. [16]	7:3	NA	C-index	Calibration curve	NA	NA	Nomograph
Zhou XJ et al. [17]	NA	NA	AUC, Sensitivity, Specificity	Calibration curve, Hosmer-Lemeshow	Bootstrap	NA	Nomograph
Liu M et al. [18]	NA	NA	AUC	Hosmer-Lemeshow, Decision curve	Bootstrap	NA	Nomograph
Dong Y et al. [19]	NA	Mean filling	AUC	Calibration curve, Decision curve	Bootstrap	Yes	Nomograph
Jia S et al. [20]	7:3	NA	AUC, Accuracy	Calibration curve, Decision curve	NA	NA	β coefficient plots the risk scoring formula
Lee DI et al. [21]	Cross verification	NA	AUC, Sensitivity, Specificity	NA	Cross verification	NA	Deep learning model based on multi-layer Perceptron architecture
Baalman SWE et al. [22]	NA	Iterative interpolation is performed by MissForest method	AUC	NA	Cross verification	NA	SHAP
Saglietto A et al. [23]	8:2	K-nearest Neighbor interpolation technique	AUC	Hosmer-Lemeshow	NA	Yes	Line computer
Sun S et al. [24]	8:2	NA	AUC, Sensitivity, Specificity	Calibration curve	Cross verification	NA	SHAP
Yang Z et al. [25]	NA	NA	AUC	Calibration curve	Bootstrap	NA	Nomograph
Sheng J et al. [26]	75:25	NA	AUC, Sensitivity, Specificity	Calibration curve	NA	NA	Nomograph
Ma XX et al. [27]	NA	NA	AUC	Calibration curve	Bootstrap	NA	Nomograph
Tang S et al. [28]	NA	Mean filling	AUC	Brier score	Cross verification	NA	Rank the importance of clinical features
Ma Y et al. [29]	7:3	Median filling	AUC, Accuracy, Recall, F1 rating	Decision curve	NA	NA	SHAP

Notes: NA: Not described; SHAP: Shapley additive explanations.