Identifying diagnostic biomarkers for Erythemato-Squamous diseases using explainable machine learning

Erythemato-squamous diseases (ESD) are a heterogeneous group encompassing six clinically and histopathologically overlapping subtypes, representing a substantial diagnostic challenge within dermatology. The existing body of research reveals a notable void in detailed examinations that deconvolute the distinct features endemic to each ESD variant. To bridge this knowledge gap, our study applied Explainable Artificial Intelligence (XAI) techniques to systematically elucidate the intricate diagnostic biomarker profiles unique to each ESD category. Methodological rigor was fortified through the employment of stratified cross-validation, bolstering the robustness and generalizability of our diagnostic model. The CatBoost classifier emerged as a preeminent algorithm within our analytical framework, manifesting exemplary classification prowess with an accuracy of 99.07%, precision of 99.12%, recall of 98.89%, and an F1 score of 98.97%. Central to our inquiry was the deployment of Shapley Additive exPlanations (SHAP) values, which afforded granular insight into the contributory weight of individual diagnostic biomarkers for each ESD subtype. Our findings delineated pivotal diagnostic biomarkers including saw-tooth appearance of retes (STAR), melanin incontinence (MI), vacuolisation and damage of basal layer (VDBL), polygonal papules (PP), and band-like infiltrate (BLI) as instrumental in the identification of seborrheic dermatitis, while Psoriasis was characterized by fibrosis of the papillary dermis (FPD), thinning of the suprapapillary epidermis (TSE), elongation of the rete ridges (ERR), clubbing of the rete ridges (CRR), and notable psoriatic spongiosis. This integrative approach, leveraging the analytical acumen of Random Forest coupled with the interpretability afforded by SHAP, signifies a significant advancement in the nuanced diagnostic landscape of ESD.