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

Table 4 The results of the first-layer models five-fold cross-validation

Models	ACC, %	MCC	AUC
PDACandCPvsHealth-MDA-RF (1-1)	88.61	0.6710	0.9427
PDACandCPvsHealth-MDA-SVM (1-2)	85.74	0.5850	0.9059
PDACandCPvsHealth-MDA-DL (1-3)	83.19	0.5267	0.8910
PDACandCPvsHealth-MDA-XGB (1-4)	89.17	0.6858	0.9475
PDACandCPvsHealth-SVD PCA Index-RF (1-5)	88.90	0.6724	0.9288
PDACandCPvsHealth-SVD PCA Index-SVM (1-6)	92.31	0.7802	0.9760
PDACandCPvsHealth-SVD PCA Index-DL (1-7)	89.72	0.7130	0.9471
PDACandCPvsHealth-SVD PCA Index-XGB (1-8)	88.89	0.6753	0.9472
PDACandCPvsHealth-FastHCS Index-RF (1-9)	84.60	0.5352	0.9025
PDACandCPvsHealth-FastHCS Index-SVM (1-10)	85.73	0.5670	0.9268
PDACandCPvsHealth-FastHCS Index-DL (1-11)	83.73	0.5386	0.8951
PDACandCPvsHealth-FastHCS Index-XGB (1-12)	86.90	0.6230	0.9250
PDACandCPvsHealth-Nipals PCA Index-RF (1-13)	88.02	0.6540	0.9274
PDACandCPvsHealth-Nipals PCA Index-SVM (1-14)	87.46	0.6306	0.9334
PDACandCPvsHealth-Nipals PCA Index-DL (1-15)	88.86	0.6957	0.9336
PDACandCPvsHealth-Nipals PCA Index-XGB (1-16)	88.89	0.6753	0.9474
PDACandCPvsHealth-Probabilistic PCA Index-RF (1-17)	89.16	0.6796	0.9478
PDACandCPvsHealth-Probabilistic PCA Index-SVM (1-18)	91.19	0.7430	0.9389
PDACandCPvsHealth-Probabilistic PCA Index-DL (1-19)	89.47	0.7163	0.9576
PDACandCPvsHealth-Probabilistic PCA Index-XGB (1-20)	88.61	0.6686	0.9106

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.