Forecasting Infant Mortality Rate in Ethiopia Using Artificial Neural Networks
Abstract
In this piece of work, the ANN approach was
applied to analyze infant mortality rate in Ethiopia. The employed annual data
covers the period 1966-2020 and the out-of-sample period ranges over the period
2021-2030. The residuals and forecast evaluation criteria (Error, MSE and MAE)
of the applied model indicate that the model is stable in forecasting infant
mortality rate in Ethiopia. The ANN (12, 12, 1) model predictions suggest that
IMR will be around 34/1000 live births per year in the out-of-sample period.
Therefore, in line with the policy recommendations, the government is
encouraged to intensify maternal and child health surveillance and control
programs in order to curb infant mortality in the country.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Thabani NYONI
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Department of Economics, University of Zimbabwe, Harare, Zimbabwe
Dan W.
Patterson (1995) Artificial Neural networks Theory and Applications. Singapore;
New York: Prentice Hall.
Fojnica,
A., Osmanoviae & Badnjeviae A (2016). Dynamic model of
tuberculosis-multiple strain prediction based on artificial neural network. In
proceedings of the 2016 5th Mediterranean conference on embedded computing pp290-293.
Kaushik AC
& Sahi. S (2018). Artificial neural network-based model for orphan
GPCRs.Neural.Comput.Appl. 29,985-992
Kishan
Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of
artificial neural networks. Cambridge, Mass.: MIT Press
Makhlysheva
A., Bakkevoll PA., Nordsletta AT & Linstad L (2018) Artificial intelligence
and machine learning in healthcare. Fact Sheet No. 9/2018 ISSN: 2535-2776
Naizhuo
Zhao., Katia Charland., Mabel Carabali., Elaine O., Nsoesie., Mathieu
MaheuGiroux., Erin Rees., Mengru Yuan., Cesar Garcia Balaguera., Gloria
Jaramillo Ramirez., & Kate Zinszer (2020). Machine learning and dengue
forecasting: Comparing random forests and artificial neural networks for
predicting dengue burden at national and sub-national scales in Colombia. PLOS
Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0008056
Smartson.
P. Nyoni, Thabani Nyoni, Tatenda. A. Chihoho (2020) Prediction of new Covid-19
cases in Ghana using artificial neural networks. IJARIIE Vol-6 Issue-6 2395-4396
Smartson.
P. Nyoni., Thabani Nyoni., Tatenda. A. Chihoho (2020) Prediction of daily new Covid-19 cases in
Egypt using artificial neural networks.IJARIIE-
Vol-6 Issue-6 2395-4396
Trishan
Panch., Peter Szolovits., & Rifat Atun (2018).Artificial intelligence,
machine learning and health systems. Viewpoints• doi: 10.7189/jogh.08.020303 5 •
Vol. 8 No. 2 • 020303
Weng SF.,
Reps J., Kai J., Garibaldi JM &Qureshi N (2017).Can machine learning
improve cardiovascular risk prediction using routine clinical data? Plos
One
Zhang G P
(2003).“Time series forecasting using a hybrid ARIMA and neural network model”,
Neurocomputing 50: 159–175.
