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DOI Prefix: 10.47001/IRJIET

Predicting Infant Mortality Rate in Botswana Using Artificial Neural Networks

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweThabani NYONISAGIT Innovation Centre, Harare, Zimbabwe

Vol 5 No 3 (2021): Volume 5, Issue 3, March 2021 | Pages: 437-440

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 01-04-2021

doi Logo doi.org/10.47001/IRJIET/2021.503075

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Abstract
In this research article, the ANN approach was applied to analyze infant mortality rate in Botswana. The employed 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 infant mortality rate in Botswana. The ANN (12, 12, 1) model predicts that  infant mortality rate will continue to decline in the country over the next 10 years. Therefore the government is encouraged consider the 7-fold policy directions suggested in this endeavor.
Keywords

ANN, Forecasting, infant mortality rate.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Predicting Infant Mortality Rate in Botswana Using Artificial Neural Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 437-440, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503075

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. Camillia R., Comeaux., MSPH., Florida A&M University (2018). Predictive Modeling for Healthcare Professionals: The use of time-series analysis for health-related data and the application of ARIMA modeling techniques in SAS for Public Health Practice SESUG Paper 245-2018.
  2. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  3. 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.
  4. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992.
  5. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks. Cambridge, Mass.: MIT Press.
  6. 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
  7. Smartson. P. Nyoni, Thabani Nyoni, Tatenda. A. Chihoho (2020) PREDICTION OF DAILY NEW COVID-19 CASES IN GHANA USING ARTIFICIAL NEURAL NETWORKS IJARIIE Vol-6 Issue-6, 2395-4396.
  8. 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.
  9. Teutsch SM & Churchill RE (2000).  Principles and Practice of Public Health Surveillance, 2nded. Oxford University.
  10. Weng SF., Reps J., Kai J., Garibaldi JM &Qureshi N (2017).Can machine learning improve cardiovascular risk prediction using routine clinical data? Plos One. 
  11. Xingyu Zhang., Tao Zhang., Alistair A., Young., & Xiaosong Li (2014). Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data PLOS ONE | www.plosone.org  | Volume 9 | Issue 2 | e88075.
  12. Yan C Q., Wang R B., Liu C H., Jiang Y (2019). Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018-2019.Zhonghua 40(6):633-637.
  13. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159–175. 

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