CO2 emission and number of hospital admissions for respiratory diseases: expected hospitalization with artificial neural networks
DOI:
https://doi.org/10.6008/CBPC2179-6858.2021.004.0047Keywords:
CO2 Emission, Respiratory tract, Artificial neural networks, Campo GrandeAbstract
This study investigated the predictive capacity of ANN’s to explain the number of hospital admissions caused by respiratory diseases in the city of Campo Grande (MS), depending on the volume of CO2 emissions and the number of inhabitants. The relevance of the study is observed by the use of artificial intelligence to assist in the prediction of respiratory diseases in the population due to the effect of the volume of CO2 emitted by motor vehicles. The study is characterized as descriptive-exploratory and the period of analysis was from 2005 to 2016. The data used in the research were obtained from SEMAGRO / MS, IBGE and DATASUS. It was concluded that the RNA model was able to explain, with an error of 0.11, the number of hospitalizations caused by respiratory diseases. It can be said that the model presented satisfactory values ​​for its validation and use.
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