Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of Sao Paulo

This work aims to obtain an artificial neural network to simulate hospitalizations for respiratory diseases influenced by pollutant gaseous such as CO, PM10, PM2.5, NO2, O-3, and SO2 emitted from 2011 to 2017, in the city of Sao Paulo. The hospitalization costs were also be calculated. MLP and RBF neural networks have been tested by varying the number of neurons in the hidden layer and the type of equation of the output function. The following pollutants and its concentration range were collected considering the supervision of Alto Tiete station set, in several neighborhoods in the city of Sao Paulo, from in the period 2011 to 2017: 28-63 mu g/m(3) of PM2.5, 52-110 mu g/m(3) of PM10, 49-135 mu g/m(3) of O-3, 0.8-2.6 ppm CO, 41-98 mu g/m(3) of NO2, and 3-16 mu g/m(3) of SO2. Results showed that a RBF neural network with 6 input neurons, 13 hidden layer neurons, and 1 output neuron, using BFGS algorithm and a Gaussian function to neuronal activation, was the best fitted to the experimental datasets. So, knowing the monthly concentration of gaseous pollutions was possible to predict the hospitalization of 1464 to 3483 +/- 510 patients, with costs between 570,447 and 1,357,151 +/- 198,171 USD per month. This way, it is possible to use this neural network to predict the costs of hospitalizing patients for respiratory diseases and to contribute to the decision-making of how much the government should spend on health care.

Automated summary

This study aims to establish an artificial neural network to predict hospitalization and costs for respiratory diseases based on the concentration of pollutant gases. Results showed that the RBF neural network was the most suitable for the experimental data, providing insights for government healthcare budget decision-making.