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. 2017 Jul;92(1):179–191. doi: 10.1016/j.kint.2017.01.017

Figure 4.

Figure 4

Local immune fingerprints in streptococcal (Strep) infections. (a) Performance of Random Forest (RF), Support Vector Machine (SVM), and artificial neural network (ANN)–based feature elimination models for the prediction of infections caused by streptococcal species (Streptococcus spp. and Enterococcus spp., N = 16) against all other episodes of peritonitis (N = 67), shown as area under the curve (AUC) depending on the number of biomarkers. One episode of peritonitis classified as streptococcal infection was a coinfection caused by Enterococcus sp. with light growth of coagulase-negative Staphylococcus spp. Red symbols depict the maximum AUC for each model. (b) Kurtosis and skewness of the top 5 biomarkers selected by RF-based feature elimination. (c) Receiver operating characteristic analysis showing specificity and sensitivity of the top 5 biomarkers. (d) Tukey plots of the top 5 biomarkers in patients with confirmed streptococcal infections and with all other episodes of peritonitis, as assessed by Mann-Whitney tests (*P < 0.05; **P < 0.01). (e) Heat map showing the top 5 biomarkers across all patients presenting with acute peritonitis. IL, interleukin; MMP, matrix metalloproteinase; TNF-β, tumor necrosis factor-β.