Fig. 7.

Construction and validation of ANN model and nomogram model. (A) ANN model constructed using feature genes, containing input layer, hidden layer and output layer. (B) Training set ROC curve with AUC = 0.948, 95% CI = 0.909–0.979, used to illustrate whether the model has good prediction performance. (C) Validation set ROC curve, AUC = 0.849, 95% CI = 0.773–0.916, used to demonstrate whether the stability and generalization of the model are good. (D) Nomogram plot of the characteristic gene construction, each element followed by a scoring scale. The scores of each element are summed to obtain a total score to predict the risk of disease. (E) Calibration curve for the evaluation of nomogram prediction performance. The higher the overlap between the solid and dashed lines and the closer the diagonal line, the better the performance. (F) Decision curve analysis (DCA), which compares the clinical benefit between the nomogram model and other diagnostic indicators. the higher the AUC, the higher the clinical benefit in the range of possible thresholds from 0 to 1.