Fig. 3.

Clustering of subjects based on the pattern of psychological and activity related parameters, obtained using unsupervised machine learning implemented as an artificial neuronal network of self-organizing maps. U-matrix visualization of distance-based structures of the serum concentration of d = 17 parameters observed in n = 373 patients. The figure has been obtained using a projection of the data points onto a toroid grid of 4,000 neurons where opposite edges are connected. The dots represent the so-called “best matching units” (BMU), i.e., neurons on the grid that after ESOM learning carried the vector that was most similar to a subjects’ data vector. The U-matrix visualization was coloured as a top view of a topographic map with brown (up to snow-covered) “heights” and green “valleys” with blue “lakes”. Watersheds indicate borderlines between different clusters. Two clusters emerged in this way, separated by the white “mountain ridge” at the left of the U-matrix. BMUs belonging to clusters #1 or #2 are coloured in black or red, respectively. The red-marked group in itself, however, might contain further subgroups. However, with the small number of data, further subgrouping was not pursued and remains subject to further research. For the given data, one can assert that the red and black marked groups are distinct and that the inner variance of the red group is larger than within the black group. The figure has been created using the R software package (version 3.5.1 for Linux; http://CRAN.R-project.org/ [25]). Specifically, the U-matrix was calculated and visualised using our R package “Umatrix” (https://cran.r-project.org/package=Umatrix [34]). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)