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

Table 2 Performance of the first-layer models on training dataset

Models	ACC, %	Sen, %	Sp, %	MCC	AUC
PDACandCPvsHealth-MDA-RF (1-1)	88.68	96.30	64.00	0.6674	0.9328
PDACandCPvsHealth-MDA-SVM (1-2)	90.57	93.83	80.00	0.7383	0.9146
PDACandCPvsHealth-MDA-DL (1-3)	87.74	88.89	84.00	0.6869	0.9151
PDACandCPvsHealth-MDA-XGB (1-4)	87.74	95.06	64.00	0.6408	0.9304
PDACandCPvsHealth-SVD PCA Index-RF (1-5)	96.23	100.00	84.00	0.8947	0.9901
PDACandCPvsHealth-SVD PCA Index-SVM (1-6)	85.85	95.06	56.00	0.5773	0.9200
PDACandCPvsHealth-SVD PCA Index-DL (1-7)	89.62	90.12	88.00	0.7363	0.9580
PDACandCPvsHealth-SVD PCA Index-XGB (1-8)	91.51	98.77	68.00	0.7549	0.9294
PDACandCPvsHealth-FastHCS Index-RF (1-9)	89.62	96.30	68.00	0.6976	0.9462
PDACandCPvsHealth-FastHCS Index-SVM (1-10)	88.68	96.30	64.00	0.6674	0.9230
PDACandCPvsHealth-FastHCS Index-DL (1-11)	82.08	88.89	60.00	0.4959	0.8993
PDACandCPvsHealth-FastHCS Index-XGB (1-12)	85.85	93.83	60.00	0.5840	0.9407
PDACandCPvsHealth-Nipals PCA Index-RF (1-13)	89.62	98.77	60.00	0.6969	0.9059
PDACandCPvsHealth-Nipals PCA Index-SVM (1-14)	89.62	97.53	64.00	0.6957	0.9294
PDACandCPvsHealth-Nipals PCA Index-DL (1-15)	90.57	95.06	76.00	0.7319	0.9679
PDACandCPvsHealth-Nipals PCA Index-XGB (1-16)	91.51	98.77	68.00	0.7549	0.9294
PDACandCPvsHealth-Probabilistic PCA Index-RF (1-17)	93.40	96.30	84.00	0.8145	0.9763
PDACandCPvsHealth-Probabilistic PCA Index-SVM (1-18)	91.51	92.59	88.00	0.7760	0.9560
PDACandCPvsHealth-Probabilistic PCA Index-DL (1-19)	88.68	90.12	84.00	0.7059	0.9462
PDACandCPvsHealth-Probabilistic PCA Index-XGB (1-20)	86.79	93.83	64.00	0.6159	0.9007

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.