Prediction of Infant Mortality in Morocco Using Artificial Neural Networks
Abstract
In this research paper, the ANN approach was
applied to analyze infant mortality rate in Morocco. The employed annual data
covers the period 1960-2020 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 Morocco. The results of study indicate that IMR will be
around 17/1000 live births per year over the next decade. Therefore, in line
with our policy advise; the government should intensify surveillance and
control programs for maternal and child health 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
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.
Henglin M.,
Stein G., Hushcha PV., Snoek J., Wiltschko AB & Cheng S (2017). Machine
learning approaches in cardiovascular imaging. Circ Cardiovasc Imaging. 2017;
10:e005614. Medline:28956772 doi:10.1161/CIRCIMAGING.117.005614
Johnson
AE., Pollard TJ., Mark RG (2017).Reproducibility in critical care: a mortality
prediction case study. Machine Learning for Healthcare Conference 2017. JMLR
W&C Track Volume 68. Available: http://proceedings.mlr.
press/v68/johnson17a/johnson17a.pdf
Kaushik AC
& Sahi. S (2018). Artificial neural network-based model for orphan
GPCRs.Neural.Comput.Appl. 29,985-992
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
Stanford
University (2017). Algorithm outperforms radiologists at diagnosing pneumonia
[Internet]. Stanford News. Available: https://news.stanford.edu/2017/11/15/algorithm-outperforms-radiologists-diagnosing-pneumonia/.
Accessed: 20 March 2018.
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,
“Time series forecasting using a hybrid ARIMA and neural network model”,
Neurocomputing 50: 159–175.
