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DOI Prefix: 10.47001/IRJIET

Tracking Sri Lanka’s Under Five Mortality Rate Using Artificial Neural Networks

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweThabani NYONIIndependent Researcher & Health Economist, Harare, Zimbabwe

Vol 6 No 7 (2022): Volume 6, Issue 7, July 2022 | Pages: 479-482

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 06-09-2022

doi Logo doi.org/10.47001/IRJIET/2022.607106

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Abstract

This study uses annual time series data on under five mortality rate for Sri Lanka from 1960 to 2021 to predict future trends of U5MR over the period 2021 to 2030. Residuals and forecast evaluation criteria indicate that the applied ANN (12, 12, 1) model is stable in forecasting under five mortality rate. ANN model projections indicate that U5MR will continue to decline throughout the out of sample period. Therefore, health authorities in Sri Lanka should continue supporting the maternal and child healthcare (MNCH) program in order to improve the quality of maternal and child healthcare services. 

Keywords

ANN, Forecasting, U5MR


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Tracking Sri Lanka’s Under Five Mortality Rate Using Artificial Neural Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 6, Issue 7, pp 479-482, July 2022. Article DOI https://doi.org/10.47001/IRJIET/2022.607106

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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