Risk factor mining and prediction of urine protein progression in chronic kidney disease: a machine learning- based study

Table 4 Performance summary

Models	AUC	95%CI		sensitivity	specificity	accuracy	AP
Models	AUC	Lower bound	Upper bound	sensitivity	specificity	accuracy	AP
XGBoost	0.844	0.798	0.891	0.735	0.835	0.768	0.920
GNB	0.808	0.755	0.861	0.762	0.780	0.768	0.893
NN	0.822	0.773	0.872	0.740	0.813	0.765	0.907
Ridge	0.836	0.788	0.884	0.790	0.769	0.783	0.918
LR	0.850	0.805	0.896	0.845	0.725	0.805	0.924
Ensemble	0.856	0.812	0.901	0.818	0.747	0.794	0.926
eGFR	0.606	0.537	0.675	0.646	0.560	0.618	0.753
Cre	0.620	0.551	0.689	0.392	0.857	0.548	0.763

ISSN: 1472-6947