Fig. 5From: Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variablesROC curves and confusion matrix for Random Forest analysis. The plots were drawn considering the following sets of variables: (i) proteomic + clinical (named “Multivariate Analysis”, blue), (ii) proteomic variables only (named “Peptides Analysis”, green), (iii) clinical variables only (named “Biological Samples”, red), (iv) PSA only (named “Univariate Analysis”, red)Back to article page