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

Figure 2.

Figure 2

Identification of local immune fingerprints associated with peritonitis caused by Gram-negative bacteria. (a) Performance of recursive feature elimination models based on Random Forest (RF), Support Vector Machines (SVM), and artificial neural networks (ANN) for the prediction of Gram-negative infections (N = 17) against all other episodes of peritonitis (N = 66), shown as area under the receiver operating characteristic curve (AUC) depending on the number of biomarkers. Red symbols depict the maximum AUC achieved 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 Gram-negative infections and with all other episodes of peritonitis, as assessed by Mann-Whitney tests (**P < 0.01). (e) Heat map showing the top 5 biomarkers across all patients presenting with acute peritonitis. IL, interleukin; TNF-α, tumor necrosis factor-α; VEGF, vascular endothelial growth factor.