BMC Medical Informatics and Decision Making

Table 4 Performance of the models

From: Discussion on machine learning technology to predict tacrolimus blood concentration in patients with nephrotic syndrome and membranous nephropathy in real-world settings

Metrics model	RF	LR	AdaBoost	GBDT	XGBoost	LGBM
Accuracy	0.6556	0.5444	0.6667	0.6556	0.7333	0.7167
F-beta	0.8221	0.5810	0.8638	0.7967	0.9124	0.9075
Recall	0.8527	0.5504	0.9147	0.8140	0.9690	0.9165
Precision	0.7190	0.7474	0.7066	0.7343	0.7396	0.8730
AUC	0.5048	0.5399	0.4770	0.5344	0.5531	0.4947

LR logistic regression; RF random forest; GBDT gradient boost decision tree; LGBM LightGBM; AUC area under the curve

Back to article page

ISSN: 1472-6947

Contact us

General enquiries: journalsubmissions@springernature.com