Figure 2. Sorted variable importance based on their contribution to random forest model classifications (grey bar) in the training data set, and area under the curve on inclusion of each predictor (dotted line) in the validation data set. Variable importance presents how important each predictor is to AF risk. For instance, age is the most important predictor, followed by albumin, uric acid, etc. AF, atrial fibrillation; ALAT, alanine transaminase; ASAT, aspartate transaminase; AUC, area under the curve; eGFR, estimated glomerular filtration rate; gamma-GT, gamma-glutamyl transferase.