Premature mortality from respiratory tract cancer (RTC) remains unevenly distributed across territories, reflecting inequalities in access to specialized healthcare services. This study applied a geographic artificial intelligence (GeoAI) framework to investigate spatially varying associations between healthcare accessibility indicators and premature RTC mortality in Southern Brazil. A population-based ecological study was conducted across 1,191 municipalities using mortality data from 2018–2022. Spatial autocorrelation was assessed using Moran’s I and Local Indicators of Spatial Association (LISA). Model performance was compared between Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Geographically Weighted Gradient Boosting Regression (GWRBoost). Spatial block cross-validation was implemented to reduce spatial leakage and overfitting. GWRBoost improved model fit (R² = 0.926) compared with OLS (R² = 0.053) and GWR (R² = 0.473), while reducing residual spatial dependence (Moran’s I = –0.048). These findings highlight the potential of geographically weighted spatial machine learning for geographically targeted cancer control strategies.
Massago et al. (Sat,) studied this question.