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 Sudan Using the Multilayer Perceptron Neural Network

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: 657-661

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 07-04-2021

doi Logo doi.org/10.47001/IRJIET/2021.503114

pdf iconFull Text PDF
Abstract

In this research article, the ANN approach was applied to analyze infant mortality rate in Sudan. 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 Sudan. The ANN (12, 12, 1) model projections suggest that infant mortality will be around 40/1000 live births per annum over the next 10 years in Sudan. The government is encouraged to intensify maternal and child health surveillance and control programs amongst other measures in order to curb infant mortality in Sudan. This can be done by adopting the suggested 7-fold policy recommendations.

Keywords

ANN, Forecasting, Infant mortality rate.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Botswana’s Art Program Success Story: Evidence from the Artificial Neural Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 657-661, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503114

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. Ahmed NK., Atiya AF., Gayar NE & El-Shishiny H (2010). An Empirical Comparison of Machine Learning Models for Time Series Forecasting. Econometric Reviews. 29(5–6):594–621. https://doi.org/10.1080/ 07474938.2010.481556
  2. Alpaydin E (2004). Machine Learning: Introduction to Machine Learning. The MIT Press; 2004.
  3. Deng L (2014). A tutorial survey of architectures, algorithms, and applications for deep learning—ERRATUM. APSIPA Transactions on Signal and Information Processing. https://doi.org/10.1017/atsip. 2013.9
  4. 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.
  5. Gabor MR & Dorgo LA (2017). Neural Networks Versus Box-Jenkins Method for Turnover Forecasting: a Case Study on the Romanian Organisation. Transformations in Business and Economics. 16(1):187– 211.
  6. Hamid SA & Habib A (2014). Financial Forecasting with Neural Networks. Academy of Accounting and Financial Studies Journal. 18(4):37–55.
  7. Hamzacebi C., Akay D., & Kutay F (2009). Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications. 2009; 36(2, Part 2):3839–3844. https://doi.org/10.1016/j.eswa.2008.02.042.
  8. Hastie T., Tibshirani R & Friedman J (2009). The elements of statistical learning: Data mining, Inference, and Prediction, Second Edition.
  9. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  10. Kock AB & Tera ¨svirta T (2016). Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques. Econometric Reviews. 35(8–10):1753–1779. https:// doi.org/10.1080/07474938.2015.1035163
  11. Makridakis S (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures. 90:46–60. https://doi.org/10.1016/j.futures.2017.03.006
  12. Marr B (2016). The Top 10 AI And Machine Learning Use Cases Everyone Should Know About, Forbes. Available from: https://www.forbes.com/sites/bernardmarr/2016/09/30/what-are-the-top-10-use-casesfor-machine-learning-and-ai/#2c292bf894c9
  13. 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
  14. Robinson C., Dilkina B., Hubbs J., Zhang W., Guhathakurta S & Brown MA (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy. 208(Supplement C):889–904. https://doi.org/10.1016/j.apenergy.2017.09.060.
  15. Salaken SM., Khosravi A., Nguyen T & Nahavandi S (2017). Extreme learning machine based transfer learning algorithms: A survey. Neurocomputing. 267:516–524. https://doi.org/10.1016/j.neucom.2017.06. 037.
  16. 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
  17. 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
  18. Spyros Makridakis., Evangelos Spiliotis., & Vassilios Assimakopoulos (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward PLoS ONE 13(3):e0194889.https://doi.org/ 10.1371/journal.pone.0194889
  19. Vapnik V (1998) “Statistical Learning Theory”, New York: Wiley.
  20. Voyant C., Notton G., Kalogirou S., Nivet ML., Paoli C & Motte F (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy.  105(Supplement C):569–582. https://doi.org/ 10.1016/j.renene.2016.12.095. 
  21. Wang J & Wang J (2017). Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Networks. 90:8–20. https://doi.org/10.1016/j.neunet.2017.03.004. PMID: 28364677
  22. Weng SF., Reps J., Kai J., Garibaldi JM &Qureshi N (2017).Can machine learning improve cardiovascular risk prediction using routine clinical data? Plos One 
  23. Yan C Q., Wang R B., Liu C H., Jiang Y (2019). Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018-2019.Zhonghua 40(6):633-637
  24. Zhang G P, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.
  25. Zhang G., Eddy Patuwo B., & Hu Y M (1998). Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting. 14(1):35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
  26. Zhang L & Suganthan PN (2016). A survey of randomized algorithms for training neural networks. Information Sciences. 364–365(Supplement C):146–155. https://doi.org/10.1016/j.ins.2016.01.039

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.