Clinically actionable classification tool for childhood and adult atopic dermatitis via explainable AI: a...

Despite the critical role of early identification in managing atopic dermatitis (AD), current studies are limited by the absence of adult-specific risk stratification models and imbalanced cohort data, hindering effective risk profiling.

We aimed to address this deficiency by developing models for AD diagnostic classification through robust machine learning techniques, with a focus on integrating personalized risk factors to improve the identification and characterization of AD in both children and adults.

The study employed multiracial cross-sectional data from the National Health And Nutrition Examination Survey (NHANES) in which children (age < 20) and adults (age ≥ 20) with self-reported AD endpoints were extracted from 1999 to 2006. Comprehensive class balancing and machine learning techniques were employed to develop a set of AD classification models for different age groups. The models were assessed using five-fold cross-validation for internal validation and internal hold-out validations using a test set composed of raw data. The optimal model for each subgroup, with the best accuracy to classify AD presence, was determined and assessed by ACC, F1-score, MCC, and ROC curve. Key features and their contributions to each classification decision are interpreted by permutation feature importance (PFI) and SHapley Additive explanations (SHAP). We compared and further analyzed demographic and nutritional indicators that have significant associations.

Of 22,635 qualified participants, 1118 (13.47%) children and 1732 (12.80%) adults were identified as having AD. The pronounced class imbalance within the dataset led to baseline models yielding accuracies in the range of merely 0.500-0.503. Following the implementation of class balancing techniques and diverse modeling methods, the refined model, utilizing accessible variables from both children and adults, achieved markedly enhanced performance (ACC: 0.835-0.885, F1-score: 0.834-0.884, AUC: 0.913-0.957). Explainable AI revealed opposing effects of dietary antioxidants between children and adults, highlighting age-specific associated mechanisms.

The classification models for both childhood and adult AD have demonstrated robust discriminative ability and potential utility for case identification in both internal and internal hold-out validations. These models address the challenge in accurate, individualized classification for childhood and adult AD, demonstrating strong discriminative potential in our validation cohorts. We have developed a novel AD diagnostic identification system specifically for both children and adults based on readily accessible demographic, clinical, and nutritional features, which enable the characterization of individuals with a high probability of having AD and the pinpointing of their associated features. This provides a methodological foundation for future research into early management strategies for AD, which could ultimately aim to minimize adverse impacts and modify disease progression.