International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

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DOI Prefix: 10.47001/IRJIET

Forecasting Infant Mortality Rate in Seychelles Using Artificial Neural Networks

Abstract

In this research work, the ANN approach was applied to analyze infant mortality rate in Seychelles. The employed annual data covers the period 1960-2020 and the out-of-sample period ranges over the period 2020-2030. 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 Seychelles. The ANN (12, 12, 1) model predictions suggest that IMR will be around 12/1000 live births per year in the next 10 years. Therefore the government is encouraged to strengthen maternal and child health surveillance systems and intensify prevention and control programs in order to significantly reduce infant mortality in the country.

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Thabani NYONI

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. Department of Economics, University of Zimbabwe, Harare, Zimbabwe

IRJIET, Volume 5, Issue 3, March 2021 pp. 617-621

doi.org/10.47001/IRJIET/2021.503106

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References

  1. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  2. 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.
  3. Hamzacebi C (2008). Improving artificial neural networks’ performance in seasonal time series forecasting, Information sciences. 178:4550-4559.
  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 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. Trashcan Panch., Peter Szolovits., & Rifat Atun (2018).Artificial intelligence, machine learning and health systems. Viewpoints•  doi: 10.7189/jogh.08.020303 5   •  Vol. 8 No. 2 •  020303
  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. Zhang G P, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.

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