Figure 3.

Identification of important features with the use of Boruta feature selection. Following collinearity correction and scaling, Boruta was applied as an artificial intelligence algorithm to select relevant features for unbiased development of machine learning classifiers. The Z-score boxplot presents rejected (red), tentative (yellow) and accepted (green) features. p < 0.01 was used as a cutoff for the selection of accepted features. Blue boxes represent Z-scores of shadow features acting as internal controls for the selection of important variables. Subsequent machine learning was performed using accepted (green) features.