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 Togo Using a Machine Learning Algorithm

Abstract

In this piece of work, the ANN approach was applied to analyze infant mortality rate (IMR) in Togo. The employed 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 rate in Togo. The applied ANN (12, 12, 1) model predictions suggest that IMR will be around 44/1000 live births per year in the coming 10 years. Therefore the government should focus on improving the quality of health care services especially primary health care and work towards developing strategies to retain its skilled health labour force.

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. 602-607

doi.org/10.47001/IRJIET/2021.503103

Full Paper
Download

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. Hamid Reza Niazkar & Majid Niazkar (2020). Application of artificial neural networks to predict the COVID-19 outbreak Global Health Research and Policy           (2020) 5:50
  4. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  5. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks
  6. 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
  7. Niazkar M (2019). Revisiting the estimation of Colebrook friction factor: a comparison between artificial intelligence models and CW based explicit equations. KSCE J Civ Eng. 23(10):4311–26. https://doi.org/10.1007/ s12205-019-2217-1.
  8. Niazkar M (2020). Assessment of artificial intelligence models for calculating optimum properties of lined channels. J Hydroinf. 2020. https://doi.org/10. 2166/hydro.2020.050.
  9. Niazkar M., Talebbeydokhti N., & Afzali S-H (2020). Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations. Scientia Iranica. 2020. https://doi.org/10.24200/SCI.2020.51432.2175.
  10. 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
  11. 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
  12. Trashcan Panch., Peter Szolovits., & Rifat Atun (2018).Artificial intelligence, machine learning and health systems. Viewpoints•  doi: 10.7189/jogh.08.020303 5   •  Vol. 8 No. 2 •  020303
  13. Vapnik V (1998) “Statistical Learning Theory”, New York: Wiley.
  14. Weng SF., 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.0174944S
  15. Zhang G P (2003), “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies