Fig. 3: Creation of the best discrimination models using AutoML in the training cohort and validation in the independent validation cohort. | British Journal of Cancer

Fig. 3: Creation of the best discrimination models using AutoML in the training cohort and validation in the independent validation cohort.

From: Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning

Fig. 3

a Schematic procedure of the index calculation in miRNA model and miRNA+CA19-9 model. b ROC curve for the performance of serum CA19-9 alone (red), miRNA model (green), and miRNA+CA19-9 model (blue) in the validation cohort. c–e Box plots of CA19-9, indices of miRNA model, and miRNA+CA19-9 model of healthy participants and pancreatic cancer patients in each stage (0, I, II, III, and IV) in the validation cohort. f, g Confusion matrices of miRNA model and miRNA+CA19-9 model at 98% specificity in each stage of the validation cohort. TP True Positive, TN True Negative, FP False Positive, FN False Negative, SEN Sensitivity, SPC Specificity, FPR False Positive Rate, FNR False Negative Rate. The color gradient indicated the rate of each metric. h, i Box plots of CEA and DUPAN-2 of healthy participants and pancreatic cancer patients in each stage (0, I, II, III, and IV) in the validation cohort.

Back to article page