Forecasting Infant Mortality Rate in Senegal Using the Multilayer Perceptron Neural Network
Abstract
In this research paper, the ANN approach was
applied to analyze infant mortality rate in Senegal. The employed annual data
covers the period 1960-2020 and the out-of-sample period ranges over the period
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 Senegal. The ANN (12, 12, 1) model predicted that IMR will be
around 33/1000 live births per year in the next 10 years. Therefore the
government is encouraged to increase coverage for child immunizations, Vitamin
A supplementation, exclusive breastfeeding for at least 6 months and intensify
maternal and child surveillance programs.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Thabani NYONI
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Department of Economics, University of Zimbabwe, Harare, Zimbabwe
Buitrago J
and Asfour S 2017 Short-Term Forecasting of Electric Loads Using Nonlinear
Autoregressive Artificial Neural Networks with Exogenous Vector Inputs 1–24
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.
Jin-ming W
and Xin-heng L (2009). The Forecast of Energy Demand on Artificial Neural
Network 2009 International Conference on Artificial Intelligence and
Computational Intelligence 31– 35
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
R
Adhiswara., A G Abdullah and Y Mulyad (2019).Long-term electrical consumption
forecasting using Artificial Neural Network (ANN) 4th Annual Applied Science
and Engineering Conference Journal of Physics: Conference Series 1402 (2019)
033081,IOP Publishing doi:10.1088/1742-6596/1402/3/033081
Reddy S S
and Momoh J A (2014). Short Term Electrical Load Forecasting Using Back
Propagation Neural Networks 2014 North American Power Symposium (NAPS) 1–6
Smartson.
P. Nyoni, Thabani Nyoni, Tatenda. A. Chihoho (2020) Prediction of daily 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
Weng SF.,
Reps J., Kai J., Garibaldi JM &Qureshi N (2017).Can machine learning
improve cardiovascular risk prediction using routine clinical data? Plos One
Yu S, Zhu K
and Diao F (2008). A dynamic all parameters adaptive BP neural networks model
and its application on oil reservoir prediction Applied Mathematics and
Computation 195 66–75
Zhang GP
(2003) Time series forecasting using a hybrid ARIMA and neural network model.
Neurocomputing 50: 159–175
Zhang S.,
Lian J., Zhao Z and Xu H (2008). Grouping Model Application on Artificial
Neural Networks for Short-term Load Forecasting Proceedings of the 7th World
Congress on Intelligent Control and Automation 6203–6206.
