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

Predicting Under Five Mortality Rate for China Using Artificial Neural Networks

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweThabani NYONIIndependent Researcher & Health Economist, Harare, Zimbabwe

Vol 6 No 7 (2022): Volume 6, Issue 7, July 2022 | Pages: 178-181

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 18-08-2022

doi Logo doi.org/10.47001/IRJIET/2022.607036

pdf iconFull Text PDF
Abstract

This study uses annual time series data on under five mortality rate (U5MR) for China from 1969 to 2020 to predict future trends of U5MR over the period 2021 to 2030. Residuals and forecast evaluation criteria of the applied model indicate that the ANN (12, 12, 1) is stable in forecasting under five mortality rate. The ANN model projections indicate that annual U5MR will continue to drop over the out of sample period. Therefore, we encourage the Chinese government to continue supporting maternal and child health programs by addressing various challenges faced by under five children in disadvantaged regions of the country. 

Keywords

ANN, Forecasting, U5MR


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Predicting Under Five Mortality Rate for China Using Artificial Neural Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 6, Issue 7, pp 178-181, July 2022. Article DOI https://doi.org/10.47001/IRJIET/2022.607036

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. UNICEF. (2019). Levels and trends in child mortality: report 2019. Estimates developed by the UN Inter-agency Group for child mortality estimation. New York: UNICEF.
  2. United Nations. (2015). transforming our world: The 2030 agenda for sustainable development, A/RES/70/1. New York: UN General Assembly.
  3. UN (2020) sustainable development goals. https://www.un.org/sustainabl development/development-agenda
  4. UNICEF (2018). Every Child alive. New York: UNICEF
  5. World Health Organization (WHO) (2019). SDG 3: Ensure healthy lives and promote wellbeing for all at all ages.
  6. United Nation. Transforming our world: The 2030 agenda for sustainable development 2016.
  7. Worldometer (2020). China demographics. https://www.worldometers.info
  8. S. Greenhalgh (2003). Science, modernity, and the making of China’s one-child policy, Popul. Dev. Rev. 29 (2) (2003) 163e196. 
  9. T. Hesketh., L. Lu., & Z.W. Xing (2005). The effect of China’s one-child family policy after 25 years, N. Engl. J. Med. 353 (11) (2005) 1171e1176.
  10. G.S. Becker & R.J. Barro (1988). A reformulation of the economic theory of fertility, Q. J. Econ. 103 (1) (1988) 1e25. 
  11. J.L. Boone & K.L. Kessler (1999). More status or more children? Social status, fertility reduction, and long-term fitness, Evol. Hum. Behav. 20 (4) (1999) 257e277. 
  12. A.Adsera (2004). Changing fertility rates in developed countries. The impact of labor market institutions, J. Popul. Econ. 17 (1) (2004) 17e43.
  13. Hong Li., ASA Yang Lu&PintaoLyu (2018). Modeling and Forecasting Chinese Population Dynamics in a MultiPopulation Context, pp 1-61.
  14. Mengqiao Wang (2019). A retrospective and predictive study of fertility rates in China from 2003 to 2018. Heliyon 5 (2019) e01460. Doi: 10.1016/j.heliyon.2019. e01460.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.