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 Kenya Using an Artificial Intelligence Technique

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweThabani NYONIDepartment of Economics, University of Zimbabwe, Harare, Zimbabwe

Vol 5 No 3 (2021): Volume 5, Issue 3, March 2021 | Pages: 632-636

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 05-04-2021

doi Logo doi.org/10.47001/IRJIET/2021.503109

pdf iconFull Text PDF
Abstract
In this study, the ANN approach was applied to analyze infant mortality rate (IMR) in Kenya. 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 Kenya. The ANN (12, 12, 1) model projections indicated that IMR will be around 30/1000 live births per year over the next 10 years. Therefore, in line with the suggested policy prescriptions; the Kenyan authorities should allocate more resources towards maternal and child health programs with the goal to capacitate primary health care facilities with medical supplies, equipment and skilled human resources in order to sufficiently tackle maternal and child health problems to curb neonatal and infant mortality in the country.
Keywords

ANN, Forecasting, infant mortality rate.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Forecasting Infant Mortality Rate in Kenya Using an Artificial Intelligence Technique” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 632-636, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503109

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. Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 2016; 316 (22): 2353–4.
  2. Bahrammirzaee A (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Comput Appl.19 (8): 1165–95.
  3. Cvetkovic B., Janko V., Romero AE., Kamal O., Stathis K., &  Lu strek  (2016). Activity recognition for diabetic patients using a smartphone. J Med Syst. 40 (12): 256.
  4. Ertel W., Black N & Mast F (2017). Introduction to Artificial Intelligence. Cham, Switzerland: Springer,2017
  5. 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
  6. Gulshan V., Peng L., & Coram M (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316 (22): 2402–10.
  7. Kaplan B (2001). Evaluating informatics applications—some alternative approaches: theory, social interactionism, and call for methodological pluralism. Int J Med Inform. 64 (1): 39–56.
  8. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  9. Lisboa PJ & Taktak AF (2006). The use of artificial neural networks in decision support in cancer: a systematic review. Neural Netw.19 (4): 408–15.
  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. Patel V L., Shortliffe E H., & Stefanelli M (2009). The coming of age of artificial intelligence in medicine. Artif Intell Med. 46 (1): 5–17.
  12. Powles J & Hodson H (2017). Google Deep Mind and healthcare in an age of algorithms. Health Technol. 7 (4): 351–67
  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. Takahashi H., Tampo H., Arai Y., Inoue Y & Kawashima H (2017). Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PLoS One 12 (6): e0179790.
  16. The Lancet (2018). Is digital medicine different? 392: 95.
  17. Topol EJ (2019). High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 25 (1): 44–56.
  18. Turing AM (1950). Computing machinery and intelligence. Mind LIX (236): 433–60.
  19. Yoonyoung Park., Gretchen Purcell Jackson., Morgan A., Foreman., Daniel Gruen., Jianying Hu  & Amar K (2020). Das1Evaluating artificial intelligence in medicine: phases of clinical research JAMIA Open, 3(3), 2020, 326–331 doi: 10.1093/jamiaopen/ooaa033
  20. Zhang G P (2003), “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175. 

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.