Fig. 5.

Selection of pain intensity and interference related features (parameters) that provide relevant information for the membership of a patient in the U-matrix based cluster #2 (see Fig. 3). Relevant parameters were identified using the Fast and Frugal decision Tree (FFT [30]) algorithm. Decision tree building was performed using 1,000 iterations with randomly resampled disjoint training and test data sets. A: Bar graph of the size of the best performing trees during the 1,000 runs of tree building. B: Bar graph displaying how many times the features were included in the best performing trees during the 1,000 runs of tree building on randomly resampled disjoint training and test data. B: Variables referring to pain in the operated area, O: variables referring to other pains, i.e., without direct relation to the operated area. C: The FFT based decision tree was built on the parameters resilience, depressive symptoms and extraversion. The figure shows the trees along with the decision limits as the basis for the assignment to either the U-matrix based cluster #2 (named “Group 2” in the tree) or to the other subjects, i.e., U-matrix based cluster # (named “Other group in the tree). D: Beanplots of the parameters algorithmically selected for the decision tree. Data are shown separately for U-matrix based cluster #1 (grey) or #2 (red). The individual observations are shown as black circles in a one-dimensional scatter plot, surrounded by the probability density function (pdf) of the distributions (coloured areas). Box and whisker plots of the same data are overlaid on the beanplots. They have been constructed using the minimum, quartiles, median (solid black red line within the box), and maximum. The whiskers add 1.5 times the interquartile range (IQR) to the 75th percentile or subtract 1.5 times the IQR from the 25th percentile. The figure has been created using the R software package (version 3.5.1 for Linux; http://CRAN.R-project.org/ [25]), the R package “FFTrees” (https://cran.r-project.org/package=FFTrees [40]), and the R package “yarr” (https://cran.r-project.org/package=yarrr [60]). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)