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 Machine Learning in the Detection of Adolescent Fertility for the Kingdom of Eswatini

Abstract

This study uses annual time series data on adolescent fertility rate for Eswatini 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 adolescent fertility will hover around 73 births per 1000 women aged 15-19 throughout the out of sample period. Therefore, we encourage the Kingdom of Eswatini to focus on improving the accessibility and affordability of sexual and reproductive health services among adolescents, strictly enforce laws that protect sexual and reproductive health rights of adolescent girls and women, and fund empowerment programs for youths.

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. 248-251

doi.org/10.47001/IRJIET/2022.612047

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