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

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Prediction of Infant Mortality Rate in Pakistan Using the Artificial Neural Network Approach

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: 642-646

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/2021.503111

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Abstract
In this research article, the ANN approach was applied to analyze infant mortality rate in Pakistan. 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 Pakistan. The ANN (12, 12, 1) model projections suggest that infant mortality will slightly decline 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 Pakistan. This may be specifically done by adopting the suggested 7-fold policy recommendations. 
Keywords

ANN, Forecasting, infant mortality.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “Prediction of Infant Mortality Rate in Pakistan Using the Artificial Neural Network Approach” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 642-646, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503111

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. Agha S (2000). The determinants of infant mortality in Pakistan. Soc Sci Med. 2000; 51(2):199-208.
  2. Al-Nahedh N (1997). Infant mortality in the rural Riyadh region of Saudi Arabia. J R Soc Health. 1997;117(2):106-9
  3. Arsh Sh., Sadeghi H., & Javid G (2001). Fetal mortality in rural area of health houses of Ardabil in 1998. J Ardabil Med Sci Uni (Persian).1(1):12-6.
  4. Esmail Nasab N., Majidzadeh S R., & Nadim A (2001). An epidemiologic study of neonatal mortality and fetal death and risk factors in Kordestan. Hakim Research Journal (Pesian). 4(4):272-7.
  5. 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.
  6. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  7. Lawoyin TO (2001). Risk factors for infant mortality in a rural African community. Soc Health.121 (2):114-8.
  8. Leach A., McArdle TF & Banya WA (1999). Neonatal mortality in a rural area of the Gambia, Ann Trop Paediatr. 19(1): 33-43.
  9. Mausner J S., Kramer S., Mausner & Bahn (1985). Epidemiology: An introductory text. 2nd ed. Philadelphia; W.B. Saunders.  pp: 100-102.
  10. Mir Fattahi M & Agha Bakhshi H (1985). Papers of UNISEF & WHO. Tehran; Medical Education Publisher (Persian).  pp 24.
  11. Muhammad Islam., Muhammad Usman., Azhar Mahmood., Aaqif Afzaal Abbasi & Oh-Young Song (2020). Predictive analytics framework for accurate estimation of child mortality rates for Internet of Things enabled smart healthcare systems. International Journal of Distributed Sensor Networks. 16(5) 1-11
  12. 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.
  13. Park JE & Park K (1997). Park’s Textbook of preventive and Social Medicine. 15th ed Jabalpur (India); M/ S Banarsidas Bhanot Publishers. P: 120.
  14. 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
  15. 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
  16. Suwal JV (2001). The main determinants of infant mortality in Nepal. Soc Sci Med. 53 (12):1667-81.
  17. UNICEF, WHO, World Bank Group, and United Nations. Levels and trends in child mortality: estimates developed by the UN inter-agency group for child mortality estimation. New York: UN Interagency Group for Child Mortality Estimation, 2017, https://data.unicef.org/ resources/levels-and-trends-in-child-mortality/ (accessed 15 April 2020).  
  18. 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
  19. Yassin KM (2000). Indices and sociodemographic determinants of childhood mortality in rural Upper Egypt. Soc Sci Med. 51(2):185-97.
  20. Zhang G P, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175. 

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