Early postnatal characteristics and differential diagnosis of choledochal cyst and cystic biliary atresia

Table 2 Comparison of diagnostic performance metrics for different machine learning models

Model name	Accuracy	AUC (95%CI)	Sensitivity	Specificity
LR
Training set	0.865	0.933 (0.9091-0.9574)	0.896	0.833
Testing set	0.948	0.986 (0.9670-1.0000)	0.857	0.963
Naive bayes
Training set	0.875	0.907 (0.8755-0.9390)	0.911	0.839
Testing set	0.896	0.955 (0.9156-0.9938)	0.929	0.890
SVM
Training set	0.898	0.965 (0.9483-0.9815)	0.885	0.911
Testing set	0.865	0.919 (0.8618-0.9762)	0.857	0.866
KNN
Training set	0.885	0.990 (0.9841-0.9951)	0.771	1.000
Testing set	0.854	0.905 (0.8435-0.9658)	0.000	1.000
Random forest
Training set	0.982	0.999 (0.9984-1.0000)	0.964	1.000
Testing set	0.917	0.948 (0.9048-0.9907)	0.857	0.927
Extra trees
Training set	0.500	1.000 (0.9995-1.0000)	0.000	1.000
Testing set	0.927	0.971 (0.9410-1.0000)	0.714	0.963
XGBoost
Training set	0.982	0.999 (0.9977-1.0000)	0.990	0.974
Testing set	0.917	0.976 (0.9504-1.0000)	0.929	0.915
LightGBM
Training set	0.938	0.983 (0.9736-0.9918)	0.984	0.891
Testing set	0.927	0.976 (0.9510-1.0000)	0.929	0.927
Gradient boosting
Training set	0.930	0.975 (0.9615-0.9894)	0.964	0.896
Testing set	0.906	0.955 (0.9143-0.9951)	0.643	0.951
AdaBoost
Training set	0.896	0.980 (0.9701-0.9900)	0.839	0.953
Testing set	0.948	0.958 (0.9079-1.0000)	0.786	0.976
MLP
Training set	0.880	0.940 (0.9172-0.9634)	0.896	0.865
Testing set	0.958	0.990 (0.9743-1.0000)	0.929	0.963

AUC: Area under the curve; CI: Confidence interval; LR: Logistic regression; SVM: Support vector machine; KNN: K-nearest neighbors; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; AdaBoost: Adaptive boosting; MLP: Multi-layer perceptron.