Inflammation-lipid metabolism biomarker-based machine learning models for predicting postoperative pulmonary metastasis in colorectal cancer

Table 2 Internal validation performance comparison of ensemble models

Algorithm	Type	AUC	AUC_95CI	AUPRC	Brier	ABscore	Accuracy	Sensitivity	Specificity	F1	PPV	NPV	Opt_Threshold
WeightedVoting	Ensemble	0.7513	0.6884-0.8149	0.5508	0.1862	0.9632	0.7303	0.7093	0.7403	0.6289	0.5648	0.8428	0.3507
SoftVoting	Ensemble	0.7513	0.6882-0.8149	0.5506	0.1862	0.9600	0.7303	0.7093	0.7403	0.6289	0.5648	0.8428	0.3512
LightGBM	Single	0.7507	0.6872-0.8157	0.5592	0.1856	0.9889	0.7191	0.7907	0.6851	0.6445	0.544	0.8732	0.3144
RotF	Single	0.7454	0.6783-0.8091	0.5333	0.1875	0.769	0.7266	0.7326	0.7238	0.6332	0.5575	0.8506	0.3459
GBDT	Single	0.7367	0.6723-0.7967	0.5439	0.1876	0.5944	0.6704	0.7791	0.6188	0.6036	0.4926	0.855	0.2922
Stacking	Ensemble	0.7321	0.6701-0.7964	0.5317	0.1897	0.3698	0.6929	0.7093	0.6851	0.598	0.5169	0.8322	0.3221
RF	Single	0.7252	0.6620-0.7862	0.5171	0.1935	0	0.6779	0.8023	0.6188	0.6161	0.5	0.8682	0.3333

AUC: Area under the curve; AUPRC: Area under the precision-recall curve; PPV: Positive predictive value; NPV: Negative predictive value; RotF: Rotation Forest; GBDT: Gradient Boosting Decision Tree; RF: Random Forest.