Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

Table 3 The performance of support vector machine models for the Classification I and Classification II

Model	Data set	Sensitivity	Specificity	PPV	NPV	AUC
Classification Scheme I*	Test	0.7715	0.7503	0.4926	0.9127	0.8347
	Training	0.7938	0.7169	0.4550	0.9211	0.8383
	10-fold cross- validation	0.7765	0.7027	0.4388	0.9130	0.8242
Classification Scheme II*	Test	0.7359	0.6254	0.5061	0.8195	0.7318
	Training	0.7092	0.6590	0.6729	0.8087	0.7393
	10-fold cross- validation	0.7059	0.6589	0.5293	0.8054	0.7357

PPV, positive predictive value; NPV, negative predictive value; AUC, area under the curve.
*See Table 1 for the definitions of Classification Schemes I and II.

ISSN: 1472-6947