Algorithm 1.

SVM-RFE embedded N-fold cross-validation

Randomly partition subjects into equal-sized N folds, each fold contains the same number of smokers and nonsmokers

For S = 1…N-fold

Exclude subjects in S-fold for testing

Initialize FeatureSet to all features

While FeatureSet is not empty

Train SVM using FeatureSet

Test SVM on S-fold

Compute weight vector of SVM

Rank features according to $w_i^2$

Remove one feature with the lowest ranking

End While

End For

Compute accuracy over all subjects for each FeatureSet size

Find the minimum FeatureSet size on which SVM get the highest accuracy