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 Burundi Using Artificial Neural Networks

Abstract

In this study, the ANN approach was applied to analyze infant mortality rate (IMR) in Burundi. The employed annual data covers the period 1964-2020 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 Burundi. The applied ANN (12,12,1) model predicted that over the next 10 years infant mortality rate will be around 38/1000 live births per year. Therefore the government should prioritize retention of skilled health labor force and capacitating primary health facilities and district hospitals with medical supplies, reliable ambulance services and neonatal & maternity equipment. This ought to be done in line with the suggested policy directions.

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. 662-666

doi.org/10.47001/IRJIET/2021.503115

Full Paper
Download

References

  1. 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
  2. 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
  3. 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
  4. Trishan 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
  5. Nyoni & Nyoni T (2019). Forecasting TB notifications at Silobela District Hospital, Zimbabwe.IJARIIE 5(6)2395-4396.
  6. Nyoni & Nyoni T (2019). Forecasting TB notifications at Zengeza clinic, Zimbabwe. Online at https://mpra.ub.uni-muenchen.de/97331/ MPRA Paper No. 97331, posted 02 Dec 2019 10:13 UTC
  7. Lingling Zhou., Ping Zhao., Dongdong Wu., Cheng Cheng and Hao Huang (2018) Time series model for forecasting the number of new admission inpatients BMC Medical Informatics and Decision Making (2018) 18:39 https://doi.org/10.1186/s12911-018-0616-8
  8. Song X., Xiao J., Deng J., Kang Q., Zhang Y & Xu J (2016). Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011. Medicine. 95(26):e3929.
  9. Wu W., Guo J., An S., Guan P., Ren Y., Xia L & Zhou B (2015). Comparison of two hybrid models for forecasting the incidence of hemorrhagic fever with renal syndrome in Jiangsu Province, China. PLoS One. 10(8):e0135492.
  10. Zhou L., Xia J., Yu L., Wang Y., Shi Y., Cai S & Nie S (2016). Using a hybrid model to forecast the prevalence of Schistosomiasis in humans. Inter J Env Res Pub Heal. 13(4):355.
  11. Siriyasatien P., Phone A., Ongruk P., Jampachaisri K & Kesorn K (2016). Analysis of significant factors for dengue fever incidence prediction. BMC Bioinformatics. 2016;17
  12. Yolcu U., Egrioglu E., & Aladag CH (2013). A new linear and nonlinear artificial neural network model for time series forecasting. Decis Support Syst. 54: 1340–7.
  13. Khashei M & Bijari M (2012). A new class of hybrid models for time series forecasting. Expert Syst Appl. 39(4):4344–57.
  14. Zhou L., Yu L., Wang Y., Lu Z., Tian L., Tan L., Shi Y., Nie S & Liu L (2014). A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. PLoS One. 9(8):e104875.
  15. Qiu M & Song Y (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PLoS One. 11(5): e0155133.
  16. Omar H., Hoang V H., & Liu DR (2016). A hybrid neural network model for sales forecasting based on ARIMA and search popularity of article titles. Comput Intell Neurosci. 2016:9656453.
  17. Zhang X., Pang Y., Cui M., Stallones L & Xiang H (2015). Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Ann Epidemiol. 25(2):101–6.
  18. Song Y., Wang Y., Liu F & Zhang Y (2017). Development of a hybrid model to predict construction and demolition waste: China as a case study. Waste Manag. 59:350–61.
  19. Araujo P., Astray G., Ferrerio-Lage JA., Mejuto J C., Rodriguez-Suarez JA., & Soto B (2011). Multilayer perceptron neural network for flow prediction. J Environ Monit. 13(1):35–41.
  20. Liu H., H-q T & Li Y-f (2012). Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl Energy. 98:415–24
  21. Nyoni S P & Nyoni T (2020). Modelling and forecasting infant deaths in Zimbabwe using ARIMA models. NOVATEUR PUBLICATIONS Journal- A Multidisciplinary Peer Reviewed Journal 6(7): 2581 - 4230.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies