Table 3 Comparison of classification performance of machine learning methods on validating and testing datasets

From: Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest

Validating data					Testing data
	AUC	SEN	F1	SPE	PPV	NPV		AUC	SEN	F1	SPE	PPV	NPV
RF	0.960	0.892	0.701	0.820	0.960	0.612		0.958	0.883	0.721	0.880	0.972	0.611
LR	0.965	0.875	0.720	0.900	0.977	0.600		0.965	0.875	0.720	0.900	0.977	0.600
SVM	0.952	0.862	0.713	0.920	0.981	0.582		0.962	0.896	0.740	0.900	0.977	0.643
NET	0.965	0.879	0.726	0.900	0.977	0.608		0.964	0.879	0.746	0.920	0.981	0.613
ELM	0.952	0.833	0.657	0.880	0.971	0.524		0.952	0.833	0.657	0.880	0.971	0.524
KNN	0.926	0.825	0.657	0.900	0.975	0.517		0.926	0.825	0.657	0.900	0.975	0.517
NB	0.940	0.871	0.672	0.820	0.959	0.569		0.940	0.871	0.672	0.820	0.959	0.569
ADAB	0.955	0.891	0.733	0.880	0.973	0.629		0.958	0.855	0.712	0.940	0.985	0.574
LOG	0.964	0.846	0.711	0.960	0.970	0.565		0.956	0.846	0.711	0.960	0.970	0.565
LDA	0.954	0.850	0.687	0.900	0.976	0.556		0.954	0.850	0.687	0.900	0.976	0.556

Back to article page

ISSN: 1756-0381

Contact us

General enquiries: journalsubmissions@springernature.com