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

Table 6 Performance of the second-layer models on independent dataset

Models	ACC, %	Sen, %	Sp, %	MCC	AUC
PDACvsCP-MDA-RF (2-1)	88.70	97.65	63.33	0.6931	0.9500
PDACvsCP-MDA-SVM (2-2)	91.30	97.65	73.33	0.7670	0.9549
PDACvsCP-MDA-DL (2-3)	89.57	90.59	86.67	0.7434	0.9282
PDACvsCP-MDA-XGB (2-4)	86.09	92.94	66.67	0.6257	0.9149
PDACvsCP-SVD PCA Index-RF (2-5)	93.04	95.29	86.67	0.8196	0.9641
PDACvsCP-SVD PCA Index-SVM (2-6)	93.04	92.94	93.33	0.8302	0.9753
PDACvsCP-SVD PCA Index-DL (2-7)	86.09	84.71	90.00	0.6888	0.9392
PDACvsCP-SVD PCA Index-XGB (2-8)	92.17	95.29	83.33	0.7951	0.9702
PDACvsCP-FastHCS Index-RF (2-9)	86.96	92.94	70.00	0.6521	0.9380
PDACvsCP-FastHCS Index-SVM (2-10)	90.43	90.59	90.00	0.7691	0.9561
PDACvsCP-FastHCS Index-DL (2-11)	86.96	89.41	80.00	0.6738	0.9149
PDACvsCP-FastHCS Index-XGB (2-12)	85.22	91.76	66.67	0.6053	0.9145
PDACvsCP-Nipals PCA Index-RF (2-13)	93.91	96.47	86.67	0.8407	0.9714
PDACvsCP-Nipals PCA Index-SVM (2-14)	86.96	94.12	66.67	0.6471	0.9604
PDACvsCP-Nipals PCA Index-DL (2-15)	86.96	89.41	80.00	0.6738	0.9192
PDACvsCP-Nipals PCA Index-XGB (2-16)	92.17	95.29	83.33	0.7951	0.9702
PDACvsCP-Probabilistic PCA Index-RF (2-17)	94.78	97.65	86.67	0.8626	0.9741
PDACvsCP-Probabilistic PCA Index-SVM (2-18)	94.78	100.00	80.00	0.8644	0.9255
PDACvsCP-Probabilistic PCA Index-DL (2-19)	86.96	91.76	73.33	0.6582	0.8859
PDACvsCP-Probabilistic PCA Index-XGB (2-20)	92.17	95.29	83.33	0.7951	0.9722

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.