I'm trying to differentiate two groups of patients using various machine learning algorithms, including support-vector machines (SVM).
As far as the details of the analysis go, I would like to train the sample on a separate group and cross-validate on another.
The problem is that patients are different in some categorical variables (gender for example) and continuous variables (age for example) none of which are of interest. In regression analysis using generalized linear models, it is easy to factor out nuisance variables. I'm wondering whether there is a way in machine learning as general, and SVM in particular to factor out the effect of nuisance variable. In some papers I have seen that authors include nuisance variable to somehow normalize them.