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

•Utilized XGBoost-BA for accurate spatio-temporal modeling of asthma-prone areas.•SHAP analysis revealed seasonal impacts of climate and air pollution on asthma.•Rainfall in spring/autumn and temperature in summer/winter were key factors.•High accuracy achieved in predicting asthma-prone areas acros...

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Veröffentlicht in:Sustainable cities and society Jg. 116; S. 105889
Hauptverfasser: Razavi-Termeh, Seyed Vahid, Sadeghi-Niaraki, Abolghasem, Ali, Farman, Naqvi, Rizwan Ali, Choi, Soo-Mi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2024
Schlagworte:
Asthma
Explainable artificial intelligence (XAI)
Healthy cities
Spatio-temporal modeling
Urban environmental factors
Urban environmental factors
Healthy cities
Explainable artificial intelligence (XAI)
Spatio-temporal modeling
Asthma
ISSN:2210-6707
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Zusammenfassung:•Utilized XGBoost-BA for accurate spatio-temporal modeling of asthma-prone areas.•SHAP analysis revealed seasonal impacts of climate and air pollution on asthma.•Rainfall in spring/autumn and temperature in summer/winter were key factors.•High accuracy achieved in predicting asthma-prone areas across all seasons. 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 (PM2.5) 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.
ISSN:2210-6707
DOI:10.1016/j.scs.2024.105889