Spatio-temporal modeling of asthma-prone areas: Exploring the influence of urban climate factors with explainable artificial intelligence (XAI)

Abstract

Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3 % in spring, 97.5 % in summer, 97.8 % in autumn, and 98.4 % in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM_2.5) in spring, carbon monoxide (CO) in summer, ozone (O₃) in autumn, and PM₁₀ in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization.