FIG 4.

Prediction of desensitization (DS) versus SU outcome at month 32 (8 weeks after MOIT discontinuation) by using baseline levels with machine learning algorithms. A, Performance metrics of models fitted with different features: epitope-specific IgE or IgG₄ binding only, SCPs (total IgE; milk-specific IgE; casein-specific IgE, IgG, and IgG₄; and β-lactoglobulin–specific IgE and IgG), or the combination of SCPs and IgE and IgG₄ epitopes in 300 bootstrapping simulations. Means and 95% CIs are presented. IgE epitopes are sufficient to achieve maximal predictive performance. Accuracy and receiver operating characteristic (ROC) curve are presented here; other metrics can be found in Fig E8. B, ROC curve and coefficients for the best model with the 6 most robust IgE epitopes identified as important predictors in at least 75% of the bootstrapping simulations (BF = 75%). C, Predicted probabilities (x-axis) versus actual MOIT outcomes of all subjects (colored bars) as predicted by using an IgE-based epitope model with a BF of 75%. If the predicted probability is greater than 0.5, the subject is classified as SU and DS otherwise. Pink and red bars represent actual MOIT outcomes: DS (pink) and SU (red). D, Performance metrics of 4 models, including epitopes with BFs of 60% to 80%. Estimates with 95% CIs are presented for cross-validation (CV) statistics.