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

Prediction of Infant Mortality Rate in Nepal

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: 696-700

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 07-04-2021

doi Logo doi.org/10.47001/IRJIET/2021.503122

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Abstract
In this research article, the ANN approach was applied to analyze infant mortality rate in Nepal. 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 infant mortality rate in Nepal. The ANN (12, 12, 1) model projections suggest that infant mortality will generally decline in Nepal over the next 10 years. The government is encouraged to intensify maternal and child health surveillance and control programs amongst other measures in order to curb infant mortality in Nepal. This can be specifically executed by adopting the suggested 7-fold policy recommendations.
Keywords

ANN, Forecasting, Infant mortality rate.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Prediction of Infant Mortality Rate in Nepal” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 696-700, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503122

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. Adhikari R., & Podhisita C (2010). Household headship and child death: evidence from Nepal. BMC int health hum rights. 10(1):13.
  2. Bindu Kumari Karki., & Guenter Kittel (2019)   Neonatal mortality and child health in a remote rural area in Nepal: a mixed methods study BMJ Pediatrics Open 2019;3 pp 1-7:e000519. doi:10.1136/ bmjpo-2019-000519
  3. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  4. 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.
  5. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  6. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks. Cambridge, Mass.: MIT Press. 
  7. MENA Report. India (2013): infant mortality rate in India. London: Albawaba (London) Ltd.
  8. Ministry of Health and Population (MOHP) (2017). Nepal demographic and health survey. Kathmandu.
  9. Ministry of Health. Nepal’s Every Newborn Action Plan Government of Nepal Ministry of Health, 2016.
  10. Moniruzzaman Uzzal (2014). Bangladesh exceeds MDG target for reducing child mortality. Dhaka, Bangladesh; 2014 [cited 27 March, 2014]. Available from: http://www.dhakatribune.com/development/2014/mar/23/bangladeshexceeds-mdg-target-reducing-child-mortality.
  11. 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
  12. Pandey J P., Dhaka MR., Karki S., Poudel P., & Pradhan MS (2013). Maternal and child health in Nepal: the effects of caste, ethnicity, and regional identity: further analysis of the 2011 Nepal demographic and health survey. http://preview.dhsprogram.com/pubs/pdf/FA73/FA73.pdf. Accessed Mar 2013
  13. Paudel Deepak., Thapa Anil., Shedain Purusotam Raj., & Paudel Bhuwan (2013). Trends and determinants of Neonatal mortality in Nepal: further analysis of demographic and health survey 2001–2011. The DHS Program 2013 [cited March 2013]; http://www.dhsprogram.com/publications/publication-fa75further-analysis.cfm.
  14. Reesha J (2013. Improving neonatal health in Nepal: major challenges to achieving millennium development goal 4. Heal Sci J 2013; 7.
  15. Reeta Lamichhane., Yun Zhao., Susan Paudel & Emmanuel O (2017). Adewuyi1Factors associated with infant mortality in Nepal: a comparative analysis of Nepal demographic and health surveys (NDHS) 2006 and 2011 BMC Public Health (2017) 17:53 pp 1-18 DOI 10.1186/s12889-016-3922-z
  16. 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
  17. 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
  18. Wang H., Liddell CA., Coates MM., Mooney MD., Levitz CE., & Schumacher AE (2013). Global, regional, and national levels of neonatal, infant, and under-5 mortality during 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 384(9947):957–79. Doi: 10.1016/S0140-6736.
  19. 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
  20. Zhang G P, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175. 

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