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

Table 3 Performance of the first-layer models on independent dataset

Models	ACC, %	Sen, %	Sp, %	MCC	AUC
PDACandCPvsHealth-MDA-RF (1-1)	91.33	98.26	68.57	0.7467	0.9606
PDACandCPvsHealth-MDA-SVM (1-2)	87.33	94.78	62.86	0.6257	0.9409
PDACandCPvsHealth-MDA-DL (1-3)	84.00	87.83	71.43	0.5714	0.9153
PDACandCPvsHealth-MDA-XGB (1-4)	90.67	96.52	71.43	0.7278	0.9595
PDACandCPvsHealth-SVD PCA Index-RF (1-5)	90.67	97.39	68.57	0.7262	0.9288
PDACandCPvsHealth-SVD PCA Index-SVM (1-6)	88.67	96.52	62.86	0.6635	0.9198
PDACandCPvsHealth-SVD PCA Index-DL (1-7)	85.33	86.09	82.86	0.6363	0.9190
PDACandCPvsHealth-SVD PCA Index-XGB (1-8)	90.67	98.26	65.71	0.7261	0.9160
PDACandCPvsHealth-FastHCS Index-RF (1-9)	84.00	93.91	51.43	0.5146	0.8737
PDACandCPvsHealth-FastHCS Index-SVM (1-10)	87.33	94.78	62.86	0.6257	0.9155
PDACandCPvsHealth-FastHCS Index-DL (1-11)	84.67	90.43	65.71	0.5672	0.9016
PDACandCPvsHealth-FastHCS Index-XGB (1-12)	86.67	93.91	62.86	0.6080	0.9058
PDACandCPvsHealth-Nipals PCA Index-RF (1-13)	90.67	98.26	65.71	0.7261	0.8961
PDACandCPvsHealth-Nipals PCA Index-SVM (1-14)	90.67	99.13	62.86	0.7276	0.9324
PDACandCPvsHealth-Nipals PCA Index-DL (1-15)	88.00	91.30	77.14	0.6716	0.9093
PDACandCPvsHealth-Nipals PCA Index-XGB (1-16)	90.67	98.26	65.71	0.7261	0.9160
PDACandCPvsHealth-Probabilistic PCA Index-RF (1-17)	91.33	96.52	74.29	0.7487	0.9165
PDACandCPvsHealth-Probabilistic PCA Index-SVM (1-18)	93.33	95.65	85.71	0.8137	0.9717
PDACandCPvsHealth-Probabilistic PCA Index-DL (1-19)	89.33	92.17	80.00	0.7081	0.9347
PDACandCPvsHealth-Probabilistic PCA Index-XGB (1-20)	89.33	93.04	77.14	0.7019	0.9426

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.