ABSTRACT Respiratory diseases remain a challenge in Brazil due to socioeconomic inequalities and environmental risks that intensify population vulnerability. This study compared XGBoost with a deep learning model using stacked Gated Recurrent Units (GRU), trained with morbidity data from respiratory diseases and exogenous variables such as per capita GDP, population density, urbanisation index and greenhouse gas emissions (1999–2023). These data were normalised and temporally disaggregated using synthetic data to refine time‐series granularity. Results showed regional heterogeneity: the GRU achieved superior performance in Porto Alegre ( R 2 = 0.529), São Paulo ( R 2 = 0.518) and São Luís ( R 2 = 0.313), while XGBoost showed mostly negative R 2 values. These findings demonstrate the potential of recurrent neural networks to capture temporal dependencies in health data and support morbidity forecasting. By anticipating fluctuations, such models can guide resource allocation and inform evidence‐based policies. The study underscores the value of integrating socioeconomic and environmental indicators into predictive frameworks and positions deep learning as a promising tool for precision public health in unequal contexts.
Nery et al. (Thu,) studied this question.