Bayesian network-based risk stratification of allergic sensitization using NHANES population data

Allergic sensitization is commonly modeled as a binary classification outcome, an approach that discards the probabilistic structure linking demographic, immunological, and clinical variables and offers limited support for individualized risk communication. This work develops and evaluates a Bayesian network (BN) for probabilistic stratification of laboratory-defined serological IgE sensitization, fitted on harmonized data from the National Health and Nutrition Examination Survey (NHANES) 2005-2006 cycle (analytic cohort n = 8,285). The network integrates demographic covariates (age, sex), immunological markers (total serum IgE, eosinophil percentage), and clinical indicators (physician-diagnosed asthma, self-reported allergic symptoms) into a directed acyclic graph whose conditional probability tables were estimated by maximum-likelihood estimation with Laplace smoothing. Serological IgE sensitization, defined as at least one allergen-specific IgE ≥ 0.35 kUA/L across a 19-allergen panel, was present in 47.1% of the cohort. A five-fold cross-validated comparison of BN, logistic regression (LR), and random forest (RF) on identical predictor sets showed indistinguishable discrimination: AUC 0.808/0.809/0.808 (IgE + eosinophils) and 0.820/0.824/0.822 (IgE + eosinophils + age + sex). Individual-level calibration was excellent: Brier score = 0.171, calibration slope = 0.9998 (95% CI 0.9563-1.0434), intercept = -0.0001 (95% CI - 0.0522 to 0.0520). The three posterior probability strata (low < 0.40, intermediate 0.40-0.70, high > 0.70) correspond structurally to the three total IgE tertiles; adding eosinophil category improves continuous discrimination by ΔAUC = +0.010 but reclassifies zero participants between strata (categorical NRI = 0). Bidirectional inference-updating sensitization probability from a confirmed asthma diagnosis-raised the posterior from 0.472 to 0.651 (ΔAUC +0.006, asthma subsample n = 8,275), confirming a structural advantage unavailable from unidirectional classifiers. Observed serological sensitization rates across strata were 13.8% (low), 46.1% (intermediate), and 81.6% (high). The framework is offered as a transparent, auditable methodological demonstration of probabilistic modeling of serological IgE sensitization. It does not model clinical allergy and should not be used to guide clinical decisions without prospective validation. Its principal advantages over discriminative classifiers are structural: bidirectional inference, modular auditability of conditional probability tables, and native uncertainty propagation.