Graphical representation of the experimental pipeline.

The process of FS and classification consists of the following steps: 1) create 100 random splits of the dataset: 75% for training and 25% for testing; 2) for each split impute both sets separately, using the imputed training set for a test set imputation; 3) after that, use the FS technique on the imputed training set, varying the size of the selected feature set (from a minimum of 2 to a maximum of 60), then choose the selected features in both sets, creating new pairs of training and test sets; 4) finally, using sets with different amounts of features, train and evaluate a ML model and choose the one with the highest AUC; record which and how many features were used for training this model, and its performance measures. Repeat these steps for all 100 random splits for each dataset and FS technique.