Hybrid CNN–GRU attention network for predicting asthma incidence in urban environments

This study aims to develop a robust deep learning framework for predicting asthma incidence by utilizing the air quality index (AQI) and environmental data, thereby enhancing proactive public health monitoring. A dual-branch architecture is proposed, combining a convolutional neural network (CNN) to extract spatial features of air pollution with a gated recurrent unit (GRU) to model temporal changes in weather conditions. An attention fusion mechanism adaptively emphasizes critical environmental factors contributing to asthma onset. The model was trained and validated using datasets from Seoul, Los Angeles, and Hanoi, covering diverse climatic and pollution patterns. Experimental results demonstrate that the proposed CNN–GRU–Attention model consistently outperforms traditional machine learning and single-branch deep learning models, achieving an area under the curve (AUC) of 0.89 and an F1-score of 0.84. These findings highlight the model’s ability to capture complex spatiotemporal dependencies between pollution and weather. The approach provides a scalable, data-driven foundation for early asthma risk warning systems and urban environmental health monitoring applications.