Prediction of Infant Mortality Rate in Gambia Using Artificial Neural Networks
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.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Thabani NYONI
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Department of Economics, University of Zimbabwe, Harare, Zimbabwe
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