Fig. 5
Performance and Feature Importance of PC Prediction Models. (A) Boxplots of residuals for RF, SVM, XGB, and GLM models in the training cohorts, with lower residuals indicating better performance. (B) Feature importance analysis of the models in the training cohort (GSE62165), highlighting the key genes contributing to predictive accuracy. (C) Cumulative distribution of residuals for each model, with steeper curves indicating better performance. (D) ROC curves for the four models in the training cohort. (E) ROC curves in the validation cohort for SVM models. (F) Nomogram predicting disease risk based on molecular markers VIM, CTNNB1, CASP9, AREG, and HIF1 A. Each marker contributes a corresponding score, and the total score correlates with overall disease risk. (G) Calibration curve comparing predicted probabilities to actual outcomes. (H) Decision curve analysis assessing the medical utility of the nomogram model, showing higher net benefit across threshold probabilities compared to no model.
