Hybrid unorganized machines to estimate the number of hospital admissions caused by PM concentration

Air pollution levels exceeding the recommended limit can be the main cause of illnesses that affect human health, mainly diseases of the respiratory system. Consequently, this high exposure can impact public health management, given the increase in hospital admissions. One of the most influential air pollution parameters related to respiratory diseases is particulate matter (PM) concentrations. Thus, this paper proposes to estimate hospital admissions due to respiratory diseases caused by PM concentration with an aerodynamic diameter less than 10 $$m (PM$$), using artificial neural networks. Three hybrid neural network models are developed by combining two architectures denoted unorganized machines: extreme learning machines and echo state networks. These models also comprise extension strategies that seek to improve the generalization capability and the variation in the nonlinear outputs. Case studies explore three cities' datasets from São Paulo state, Brazil: Cubatão, Campinas, and São Paulo, to assess the quality of the hospital admissions estimations obtained by applying the proposed models. Results demonstrate that the hybrid models outperform the previously developed standard approaches in several scenarios. An overall analysis shows that the hybrid models can be a suitable strategy considering the instance particularities, especially in large datasets.