I've trained a Random Forest model on a dataset of 60 protein predictors for healthy controls (label 0) and cancer patients (label 1).
I then tested this model on a dataset of at-risk patients divided into those who later got cancer (label 1), and those who didn't (label 0).
My model's performance gave an AUC-ROC of 0.4.
Other threads and papers (linked below), say that for AUC < 0.5, a classifier has useful information but is applying it incorrectly. People seem to suggest reversing the labels, to give an AUC-ROC of 0.6 Can AUC-ROC be between 0-0.5 http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf
However, would this be appropriate in this case? Reversing the test dataset labels would mean giving the at-risk individuals who stayed healthy a label of 1 (the same as the cancer patients in the training data), which doesn't seem correct to me??