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 Egypt Using a Machine Learning Technique

Abstract

In this research paper, the ANN approach was applied to analyze infant mortality rate (IMR) in Egypt. 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 IMR in Egypt. The applied ANN (12,12, 1) model predicted that IMR will be around 17/1000 live births per year in the out-of-sample period. Therefore the government in line with our policy suggestions, is encouraged to allocate more resources for primary healthcare in order to provide quality maternal and neonatal care especially in rural areas.

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Thabani NYONI

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. Department of Economics, University of Zimbabwe, Harare, Zimbabwe

IRJIET, Volume 5, Issue 3, March 2021 pp. 686-690

doi.org/10.47001/IRJIET/2021.503120

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References

  1. CDC (Centers for Disease Control and Prevention). 2008. National Center for Injury Prevention and Control: Web-Based Injury Statistics Query and Reporting System (WISQARS) [online]. Leading causes of death reports, 1999-2005. Accessed October 15, 2008. http://webapp.cdc.gov/sasweb/ncipc/leadcaus.html
  2. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  3. 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.
  4. Hyattsville, MD: Public Health Service. <http://www.cdc.gov/nchs/data/hp2000/hp2k01-acc.pdf>
  5. Jahidur Rahman Khan & Nabil Awan (2017). A comprehensive analysis on child mortality and its determinants in Bangladesh using frailty models Archives of Public Health (2017) 75:58: 1-10 DOI10.1186/s13690-017-0224-6. pp1-10
  6. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  7. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks
  8. Naizhuo Zhao., Katia Charland., Mabel Carabali., Elaine O., Nosy., Mathieu MaheuGiroux., Erin Rees., Mengru Yuan., Cesar Garcia Beleaguerer., 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
  9. NCHS. 2001. Healthy people 2000 final review.
  10. Rubayet S., Shahidullah  M., Hossain A., Corbett E., Moran AC., Mannan I., Matin Z., Wall SN., Pfitzer A., & Mannan  I (2012). Newborn survival in Bangladesh: a decade of change and future implications. Health Policy Plan.2012; 27(suppl_3):40–56.
  11. 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
  12. 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
  13. Sukma Elida., Siti Maisyaroh., Fitri Siregar., Arafat Huns’., Dian Fer.,& Azwar Azwar  (2019).The Influence of Maternal Age, Parity and Education on Infant Mortality in  West Aceh Regency J-KESMAS  6(2)  pp 1-6
  14. UNFPA (2016). Population situation analysis of Egypt:https://Egypt.unfpa.org/sites/default/files/pub-pdf/Population situation analysis. WEB May 23rd pdf.
  15. USAID (2005). Ministry of Health and Population Egypt Service Provision assessment survey 2004.
  16. Weng S F., Reps J., Kai J., Garibaldi J M., & Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE 12(4): e0174944. https://doi.org/10.1371/journal.pone.0174944
  17. WHO (2006). EMRO health system profile: Egypt
  18. World Bank (2019).Population estimates and projections/Data Bank 2019.
  19. Zhang G P (2003), “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175. 

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