Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study

Table 2 Performance of four machine learning classifiers (support vector machine, multi-layer perceptron, k-nearest neighbor and logistic regression)

Classifiers	Training set				Test set
	AUC	95%CI	Sensitivity	Specificity	AUC	95%CI	Sensitivity	Specificity
ASVM	0.904	0.865-0.943	0.840	0.885	0.890	0.794-0.987	0.857	0.850
AKNN	0.790	0.736-0.844	0.638	0.791	0.762	0.640-0.884	0.667	0.725
AMLP	0.821	0.769-0.873	0.840	0.649	0.814	0.691-0.937	0.810	0.750
ALR	0.788	0.731-0.846	0.723	0.730	0.750	0.607-0.893	0.714	0.750
VSVM	0.890	0.850-0.930	0.926	0.703	0.867	0.778-0.956	0.810	0.800
VKNN	0.834	0.783-0.884	0.702	0.838	0.790	0.676-0.904	0.667	0.800
VMLP	0.800	0.744-0.856	0.766	0.730	0.769	0.648-0.890	0.571	0.850
VLR	0.760	0.698-0.822	0.628	0.777	0.735	0.606-0.863	0.999	0.375

ASVM: Arterial support vector machine; AKNN: Arterial k-nearest neighbor; AMLP: Arterial multilayer perceptron; ALR: Arterial logistic regression; VSVM: Venous support vector machine; VKNN: Venous k-nearest neighbor; VMLP: Venous multilayer perceptron; VLR: Venous logistic regression.