Integrating ultrasound and serum indicators for evaluating outcomes of targeted immunotherapy in advanced liver cancer

Table 2 Performance of various machine learning algorithms in the internal validation group

Model	AUC	AUC 95%CI	Accuracy	Sensitivity	Specificity	PPV	NPV
Random forest	0.975	0.924-0.998	0.951	0.727	0.981	0.957	0.944
Gradient boosting	0.864	0.798-0.902	0.983	0.909	0.852	0.942	0.980
LightGBM	0.834	0.801-0.895	0.967	0.818	0.891	0.944	0.962
Voting classifier	0.902	0.881-0.972	0.983	0.909	0.948	0.879	0.980
Support vector classifier	0.822	0.728-0.915	0.967	0.818	0.927	0.943	0.962
Logistic regression	0.728	0.708-0.857	0.967	0.909	0.980	0.909	0.980
XGBoost	0.851	0.799-0.924	0.983	0.909	0.893	0.915	0.980
Extra trees	0.901	0.854-0.95	0.935	0.727	0.980	0.888	0.943
K-nearest neighbors	0.798	0.705-0.867	0.887	0.636	0.941	0.701	0.923
Decision tree	0.837	0.757-0.903	0.919	0.95	0.901	0.687	0.913
Naive Bayes	0.878	0.797-0.960	0.838	0.818	0.843	0.529	0.955
AdaBoost	0.868	0.783-0.952	0.919	0.818	0.941	0.751	0.962
Ridge classifier	0.842	0.798-0.895	0.967	0.909	0.980	0.909	0.980

AUC: Area under the curve; CI: Confidence interval; PPV: Positive predict value; NPV: Negative predict value; LightGBM: Light gradient boosting machine; XGBoost: Extreme gradient boosting.