Figure 6
From: Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers
Classification results of an SVM-based classifier. Male and female datasets of size 7, 20, 33, and 67 each were compared. Features (selected based on a p-value < 0.05 in the unadjusted WT) were combined into respective biomarker models (M7, M20, M33, and M67). Their performance was initially assessed by complete leave-one-out cross validation leading to an accuracy of 100%, 95%, 84% and 94%, respectively, erroneously indicating optimal performance of the M7 model. The ROC analysis shows the results when these models are tested on an independent set of 134 samples. As is evident and expected, best performance can be observed when employing the M67 model, while the M7 model barely exceeds the results obtained by mere guessing (The area under the curve AUC for the models M7, M20, M33 and M67 is 0.715, 0.786, 0.900 and 0.937, respectively).