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 Burkina Fasso Using Artificial Neural Networks

Abstract

In this research paper, the ANN approach was applied to analyze infant mortality rate in Burkina Fasso. 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 Burkina Fasso. The ANN (12, 12, 1) model predicted that over the out-of-sample period the rate of infant mortality will be around 53/1000 live births per year. Therefore we implore the government to strengthen surveillance in maternal and child health programs and allocate more resources towards improving primary health care in the country. This must be done in line with suggested policy recommendations.

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Thabani NYONI

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. SAGIT Innovation Centre, Harare, Zimbabwe

IRJIET, Volume 5, Issue 3, March 2021 pp. 419-423

doi.org/10.47001/IRJIET/2021.503072

Full Paper
Download

References

  1. Althouse BM &Ng YY (2011). Cummings DAT, Prediction of dengue incidence using serach query surveillance. Ploys Neglected Tropical Diseases 2011; 5:e1258. https://doi.org/10.1371/journal.pntd.0001258 PMID: 21829744
  2. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  3. Daniel Zeng., Zhidong Cao & Daniel B Neil (2021). Artificial intelligence enabled public health surveillance—from local detection to global epidemic monitoring and control. ELSEVIER pp 1-18.
  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. Gambhir S., Malik SK., & Kumar Y (2018). The diagnosis of dengue disease: An evaluation of three machine learning approaches. International Journal of Healthcare Information Systems and Informatics 2018; 13:1–19. https://doi.org/10.4018/ijhisi.2018040101 PMID: 3
  6. Guo P., Liu T., Zhang Q., Wang L., Xiao J & Zhang Q (2017). Developing a dengue forecast model using machine learning: A case study in China. PLoS Neglected Tropical Diseases 11:e0005973. https://doi.org/10.1371/journal.pntd.0005973 PMID: 29036169
  7. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  8. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks
  9. Laureano-Rosario AE., Duncvan AP., Mendez-Lazaro PA., Garcia-Rejon JE., Gomez-Carro S., & Farfan-Ale J (2018). Application of artificial neural networks for dengue fever outbreak predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Disease 2018;3:5
  10. 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
  11. Nyoni S P & Nyoni T (2020). Modelling and forecasting infant deaths in Zimbabwe using ARIMA models. NOVATEUR PUBLICATIONS JournalNX- A Multidisciplinary Peer Reviewed Journal ISSN No: 2581 - 4230 VOLUME 6, ISSUE 7.
  12. Scavuzzo JM., Trucco F., Espinosa M., Tauro C B., Abril M., & Scavuzzo CM (2018). Modeling dengue vector population using remotely sensed data and machine learning. Acta Tropica 185:167–175. https://doi.org/10.1016/j.actatropica.2018.05.003 PMID: 29777650
  13. 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
  14. 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
  15. 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
  16. Weng SF, Reps J, Kai J, Garibaldi JM, 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. 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
  18. 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