Figure 5.

Classification accuracy of random forest classifiers with varying sizes of input feature sets. Pink bars indicate the accuracy in classifying active giant cell arteritis (GCA) versus controls, and grey represent accuracy in classifying inactive GCA versus controls. This process involved preselecting a specified number of input plasma protein features. Different numbers of top-selected features were chosen in each training set fold, ranging from 10 to 250. These selected features were then applied in a random forest classifier, with accuracy assessed on the test set across all ten folds of cross-validation.