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

Demonstrating Use of a Machine Learning Technique in Adolescent Health Policy-Making in the Gambia

Abstract

This study uses annual time series data on adolescent fertility rate for the Gambia from 1960 to 2020 to predict future trends of adolescent fertility rate over the period 2021 to 2030. The forecast evaluation criteria of the applied model indicate that the ANN (12, 12, 1) model is stable in forecasting adolescent fertility rate. The neural network model projections revealed that adolescent fertility rate will decline throughout the out of sample period. Therefore, we encourage the government of Gambia to scale up awareness programs in the community, strictly enforce laws that protect sexual and reproductive health rights for women, channel more funds towards youth empowerment programs and promote girl child education.

Country : Zimbabwe

1 Smartson. P. NYONI2 Thabani NYONI

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. Independent Researcher & Health Economist, Harare, Zimbabwe

IRJIET, Volume 6, Issue 12, December 2022 pp. 276-280

doi.org/10.47001/IRJIET/2022.612052

Full Paper
Download

References

  1. World Health Organization (2017). Global Accelerated Action for the Health of Adolescents (AA- HA!): Guidance to Support Country Implementation. Geneva: World Health Organization, 2017:151. http://www. who. int/ maternal_ child_ adolescent/ topics/ adolescence/ framework- accelerated- action/ en/
  2. Patton G.C., Sawyer S.M., Santelli J.S (2016). Our future: a Lancet commission on adolescent health and wellbeing. Lancet. 387:2423–78.
  3. Steinberg L., Icenogle G., and Shulman EP (2018). Around the world, adolescence is a time of heightened sensation seeking and immature self- regulation. DevSci 2018; 21.
  4. Mokdad A.H., Forouzanfar M.H., and Daoud F (2016). Global burden of diseases, injuries, and risk factors for young people’s health during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2016; 387:2383–401.
  5. Ganchimeg T., Ota E., Morisaki N., Laopaiboon M., Lumbiganon P., Zhang J (2014). Pregnancy and childbirth outcomes among adolescent mothers: a World Health Organization multicountry study. BJOG Int J ObstetGynaecol.121(Suppl 1):40–8.
  6. Chen X.K., Wen S.W., Fleming N., Demissie K., Rhoads G.G., and Walker M (2007). Teenage pregnancy and adverse birth outcomes: a large population based retrospective cohort study. Int J Epidemiol. 36(2):368–73.
  7. Kurth F., Bélard S., Mombo-Ngoma G.., Schuster K., Adegnika A.A., Bouyou-Akotet M.K (2010). Adolescence as Risk Factor for Adverse Pregnancy Outcome in Central Africa – A Cross-Sectional Study. PLOS ONE. 20(12):e14367.
  8. United Nations (2015). transforming our world: The 2030 agenda for sustainable development, A/RES/70/1. New York: UN General Assembly.
  9. World Health Organization (WHO) (2019). SDG 3: Ensure healthy lives and promote wellbeing for all at all ages.
  10. United Nations (2016). Transforming our world: The 2030 agenda for sustainable development.
  11. Ogawa K., Matsushima S., Urayama K.Y., Kikuchi N., Nakamura N., and Tanigaki S (2019). Association between adolescent pregnancy and adverse birth outcomes: A multicenter cross sectional Japanese study. Sci Report 2019; 9:2365.
  12. Karataşl

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies