Use of an artificial neural network for detecting excess deaths due to cholera in Ceará, Brazil

Revista de saúde pública
Maria Lúcia F Penna

Abstract

To evaluate recurrent neural networks as a predictive technique for time-series in the health field. The study was carried out during a cholera epidemic which took place in 1993 and 1994 in the state of Ceará, northeastern Brazil, and was based on excess deaths having 'poorly defined intestinal infections' as the underlying cause (ICD-9). The monthly number of deaths with due to this cause between 1979 and 1995 in the state of Ceará was obtained from the Ministry of Health's Mortality Information System (SIM). A network comprising two neurons in the input layer, twelve in the hidden layer, one in the output layer, and one in the memory layer was trained by backpropagation using the fist 150 observations, with 0.01 learning rate and 0.9 momentum. Training was ended after 22,000 epochs. We compare the results with those of a negative binomial regression. ANN forecasting was adequate. Excessive mortality (number of deaths above the upper limit of the confidence interval) was detected in December 1993 and October/November 1994. However, negative binomial regression detected excess mortality from March 1992 onwards. The artificial neural network showed good predictive ability, especially in the initial period, and was able to detect...Continue Reading

References

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Citations

Jan 29, 2017·Current Environmental Health Reports·Antarpreet JutlaRita Colwell
Jan 19, 2017·Revista de saúde pública·Eliseu Alves Waldman, Ana Paula Sayuri Sato
Oct 16, 2013·Cadernos de saúde pública·Thainá Alves MalhãoRoberto de Andrade Medronho
Mar 1, 2011·Revista Brasileira De Ortopedia·Carlos Cassiano Denipotti VeroneziCristian Cechinel
Nov 19, 2010·Revista Da Sociedade Brasileira De Medicina Tropical·Guilherme Bernardino da CunhaSelma Terezinha Milagre

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