International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Forecasting Infant Mortality Rate in Algeria Using Artificial Neural Networks

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweThabani NYONISAGIT Innovation Centre, Harare, Zimbabwe

Vol 5 No 3 (2021): Volume 5, Issue 3, March 2021 | Pages: 460-463

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 01-04-2021

doi Logo doi.org/10.47001/IRJIET/2021.503079

pdf iconFull Text PDF
Abstract
In this research article, the ANN approach was applied to analyze infant mortality rate in Algeria. 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 Algeria. The ANN (12, 12, 1) model projections suggest that infant mortality will be around 21 infant deaths per 1000 live births per annum over the next 10 years in Algeria. The government is encouraged to intensify maternal and child health surveillance and control programs amongst other measures in order to curb infant mortality in Algeria. This can be specifically done by adopting the suggested 7-fold policy recommendations.
Keywords

ANN, Forecasting, Infant mortality.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Forecasting Infant Mortality Rate in Algeria Using Artificial Neural Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 460-463, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503079

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  2. 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.
  3. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992.
  4. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks. Cambridge, Mass.: MIT Press. 
  5. 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
  6. Nelson, M., Illingworth, W.T., 1990. A Practical Guide To Neural nets. In: Proceedings of Intelligent Engineering Nets. Addison-Wesley, Reading, MA
  7. Sakhi Kohli., Surbhi Miglani., & Rahul Rapariya (2014). BASICS OF ARTIFICIAL NEURAL NETWORK , International Journal of Computer Science and Mobile Computing, Vol.3 Issue.9, September- 2014, pg. 745-751
  8. Schalkoff, R.J., 1997. Artificial Neural Networks. McGraw-Hill. New York. 
  9. 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
  10. 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.
  11. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159–175. 

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.