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DOI Prefix: 10.47001/IRJIET

Prediction of Infant Mortality Rate in Gambia Using Artificial Neural Networks

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: 587-591

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/2021.503100

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Abstract
In this research article, the ANN approach was applied to analyze infant mortality rate (IMR) in Gambia. 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 Gambia. The applied ANN (12, 12, 1) model projected that IMR will generally be around 34/1000 live births per year in the out-of-sample period. Therefore the Gambian government is encouraged to allocate more resources towards maternal and child health programs with the aim of retaining skilled labor force in primary health care and referral hospitals, procuring medical supplies  needed for maternity emergencies and essential newborn care, and continuous health education among communities.
Keywords

ANN, Forecasting, infant mortality rate.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Prediction of Infant Mortality Rate in Gambia Using Artificial Neural Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 587-591, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503100

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
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