  Oral Food Challenges (OFCs) are essential to accurately diagnosing food
allergy due to the limitations of existing clinical testing. However, some
patients are hesitant to undergo OFCs, while those willing suffer from limited
access to allergists in rural/community healthcare settings. Despite its
success in predicting patient outcomes in other clinical settings, few
applications of machine learning to food allergy have been developed. Thus, in
this study, we seek to leverage machine learning methodologies for OFC outcome
prediction. Retrospective data was gathered from 1,112 patients who
collectively underwent a total of 1,284 OFCs, and consisted of clinical factors
including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests
(SPTs), comorbidities, sex, and age. Using these features, multiple machine
learning models were constructed to predict OFC outcomes for three common
allergens: peanut, egg, and milk. The best performing model for each allergen
was an ensemble of random forest (egg) or Learning Using Concave and Convex
Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve
(AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and
milk, respectively. Moreover, all such models had sensitivity and specificity
values 89%. Model interpretation via SHapley Additive exPlanations (SHAP)
indicates that specific IgE, along with wheal and flare values from SPTs, are
highly predictive of OFC outcomes. The results of this analysis suggest that
ensemble learning has the potential to predict OFC outcomes and reveal relevant
clinical factors for further study.
