Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution

Bibliographic Data

ID10723715
AuthorsLuciano Eustáquio Chaves (Universidade Estadual Paulista, Brasil; Faculdade de Pindamonhangaba, Brasil), Luiz Fernando Costa Nascimento (0009-0007-6833-9544, Universidade de Taubaté, Brasil; Universidade Estadual Paulista, Brasil), Paloma Maria Silva Rocha Rizol (0000-0001-5246-4438, Universidade Estadual Paulista (Unesp))
Year2017
Volume51
Issue0
Pages55-55
Publication date2017-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueRevista de Saúde Pública (JOURNAL)
Journal identifiersISSN: 0034-8910 • E-ISSN: 0034-8910
PublisherFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/s1518-8787.2017051006501
PMID28658366
OpenAlexW2716681365
SCIELO_PIDS0034-89102017000100244
LanguageEN
Citations received1
References cited19

OBJECTIVE Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State. METHODS This is a computational model using fuzzy logic based on Mamdani’s inference method. For the fuzzification of the input variables of particulate matter, ozone, sulfur dioxide and apparent temperature, we considered two relevancy functions for each variable with the linguistic approach: good and bad. For the output variable number of hospitalizations for asthma and pneumonia, we considered five relevancy functions: very low, low, medium, high and very high. DATASUS was our source for the number of hospitalizations in the year 2007 and the result provided by the model was correlated with the actual data of hospitalization with lag from zero to two days. The accuracy of the model was estimated by the ROC curve for each pollutant and in those lags. RESULTS In the year of 2007, 1,710 hospitalizations by pneumonia and asthma were recorded in São José dos Campos, State of São Paulo, with a daily average of 4.9 hospitalizations (SD = 2.9). The model output data showed positive and significant correlation (r = 0.38) with the actual data; the accuracies evaluated for the model were higher for sulfur dioxide in lag 0 and 2 and for particulate matter in lag 1. CONCLUSIONS Fuzzy modeling proved accurate for the pollutant exposure effects and hospitalization for pneumonia and asthma approach

Air pollutants · Air pollution · Asthma · Environmental health · Fuzzy logic · Lag · Overdispersion · Particulates · Pneumonia · Receiver operating characteristic · Statistics · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Artificial Intelligence · Computer Science · Internal Medicine · Maternal and Neonatal Healthcare · Mathematics · Medicine · Pediatrics

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