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

Table 5 Performance of the second-layer models on training dataset

Models	ACC, %	Sen, %	Sp, %	MCC	AUC
PDACvsCP-MDA-RF (2-1)	90.12	95.00	76.19	0.7363	0.9444
PDACvsCP-MDA-SVM (2-2)	93.83	96.67	85.71	0.8372	0.9563
PDACvsCP-MDA-DL (2-3)	85.19	90.00	71.43	0.6143	0.9159
PDACvsCP-MDA-XGB (2-4)	86.42	91.67	71.43	0.6412	0.9246
PDACvsCP-SVD PCA Index-RF (2-5)	93.83	96.67	85.71	0.8372	0.9802
PDACvsCP-SVD PCA Index-SVM (2-6)	93.83	98.33	80.95	0.8357	0.9627
PDACvsCP-SVD PCA Index-DL (2-7)	88.89	90.00	85.71	0.7266	0.9159
PDACvsCP-SVD PCA Index-XGB (2-8)	86.42	90.00	76.19	0.6521	0.9603
PDACvsCP-FastHCS Index-RF (2-9)	93.83	98.33	80.95	0.8357	0.9563
PDACvsCP-FastHCS Index-SVM (2-10)	88.89	93.33	76.19	0.7065	0.9750
PDACvsCP-FastHCS Index-DL (2-11)	93.83	96.67	85.71	0.8372	0.9333
PDACvsCP-FastHCS Index-XGB (2-12)	93.83	98.33	80.95	0.8357	0.9357
PDACvsCP-Nipals PCA Index-RF (2-13)	92.59	93.33	90.48	0.8145	0.9651
PDACvsCP-Nipals PCA Index-SVM (2-14)	87.65	93.33	71.43	0.6698	0.9397
PDACvsCP-Nipals PCA Index-DL (2-15)	88.89	91.67	80.95	0.7155	0.9246
PDACvsCP-Nipals PCA Index-XGB (2-16)	86.42	90.00	76.19	0.6521	0.9603
PDACvsCP-Probabilistic PCA Index-RF (2-17)	93.83	95.00	90.48	0.8422	0.9698
PDACvsCP-Probabilistic PCA Index-SVM (2-18)	96.30	100.00	85.71	0.9035	0.9833
PDACvsCP-Probabilistic PCA Index-DL (2-19)	91.36	96.67	76.19	0.7680	0.9579
PDACvsCP-Probabilistic PCA Index-XGB (2-20)	90.12	91.67	85.71	0.7520	0.9563

ACC: Accuracy; Sen: Sensitivity; Sp: Specificity; 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.