Early cancer diagnosis via interpretable two-layer machine learning of plasma extracellular vesicle long RNA

Table 7 The results of the second-layer models five-fold cross-validation

Models	ACC, %	MCC	AUC
PDACvsCP-MDA-RF (2-1)	89.60	0.7239	0.9431
PDACvsCP-MDA-SVM (2-2)	91.44	0.7739	0.9573
PDACvsCP-MDA-DL (2-3)	89.21	0.7293	0.9493
PDACvsCP-MDA-XGB (2-4)	88.09	0.6791	0.9157
PDACvsCP-SVD PCA Index-RF (2-5)	89.60	0.7209	0.9066
PDACvsCP-SVD PCA Index-SVM (2-6)	91.42	0.7796	0.9298
PDACvsCP-SVD PCA Index-DL (2-7)	85.14	0.6272	0.8874
PDACvsCP-SVD PCA Index-XGB (2-8)	82.54	0.5395	0.8957
PDACvsCP-FastHCS Index-RF (2-9)	87.38	0.6612	0.9071
PDACvsCP-FastHCS Index-SVM (2-10)	88.11	0.6882	0.9345
PDACvsCP-FastHCS Index-DL (2-11)	87.37	0.6672	0.9063
PDACvsCP-FastHCS Index-XGB (2-12)	86.62	0.6449	0.9117
PDACvsCP-Nipals PCA Index-RF (2-13)	89.22	0.7158	0.9130
PDACvsCP-Nipals PCA Index-SVM (2-14)	91.06	0.7684	0.9109
PDACvsCP-Nipals PCA Index-DL (2-15)	86.25	0.6401	0.8944
PDACvsCP-Nipals PCA Index-XGB (2-16)	82.54	0.5395	0.8964
PDACvsCP-Probabilistic PCA Index-RF (2-17)	98.13	0.9520	0.9956
PDACvsCP-Probabilistic PCA Index-SVM (2-18)	92.94	0.8195	0.9502
PDACvsCP-Probabilistic PCA Index-DL (2-19)	88.11	0.6951	0.9519
PDACvsCP-Probabilistic PCA Index-XGB (2-20)	82.91	0.5606	0.9011

ACC: Accuracy; MCC: Mathew’s correlation coefficient; AUC: Area under the receiver operating characteristic curve; PDAC: Pancreatic ductal adenocarcinoma; CP: Chronic pancreatitis; MDA: Mean decrease in accuracy; RF: Random forest; SVM: Support vector machine; DL: Deep learning; XGB: Extreme gradient boosting; SVD: Singular value decomposition; PCA: Principal component analysis; FastHCS: High-dimensional congruent subsets; Nipals: Nonlinear iterative partial least squares.