Analysis of margin distances identified by support vector machine analysis. The margin distances quantified from the support vector machine ranked, from greatest to least distance of separation, between non-sensitizer treatment groups and sensitized groups for metrics from a Bioplex screen using supernatant from co-culture of RealSkin with MUTZ-3 derived Langerhan’s cells (RSLC). The accuracy, sensitivity, and specificity for each cytokine metric were determined using a support vector machine classification model with 10-fold cross. The top ten secretion metrics (IL-12, IL-9, VEGF, IFN-γ, IL-4, PDGF, IL-8, IL-7, GM-CSF, and IL-6) that can accurately classify non-sensitizers (vehicle and salicylic acid (SA)) from sensitizers (isoeugenol (IE) and p-phenylenediamine (PPD)) identified by SVM all have p values ≤ 0.05 as determined by ANOVA. IL-12 is the only metric that has an accuracy, sensitivity, and specificity value that exceeds 90%. Data analyzed by the SVM included all treatment conditions (untreated, vehicle, SA, IE, and PPD) and all of their respective concentrations for N = 3 independent replicates.