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Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland

Author

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  • Maria Tzitiridou-Chatzopoulou

    (School of Healthcare Sciences, Midwifery Department, University of Western Macedonia, 50100 Kozani, Greece)

  • Georgia Zournatzidou

    (Department of Business Administration, University of Western Macedonia, 50100 Kozani, Greece)

  • Michael Kourakos

    (School of Healthcare Sciences, Department of Nursing, University of Ioannina, 45500 Ioannina, Greece)

Abstract

The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values. However, it may be impacted by macroeconomic trends in the short term, although these effects are likely to be minimal when fertility is low. With the objective of forecasting monthly deliveries, this study concentrates on the analysis of registered births in Scotland. Through this approach, we examine the significance of precisely forecasting fertility trends, which can subsequently aid in the anticipation of demand in diverse sectors by allowing policymakers to anticipate changes in population dynamics and customize policies to tackle emerging demographic challenges. Consequently, this has implications for fiscal stability, national economic accounts and the environment. In conducting our analysis, we incorporated non-linear machine learning methods alongside traditional statistical approaches to forecast monthly births in an out-of-sample exercise that occurs one step in advance. The outcomes underscore the efficacy of machine learning in generating precise predictions within this particular domain. In sum, this research will comprehensively demonstrate a cutting-edge model of machine learning that utilizes several attributes to assist in clinical decision-making, predict potential complications during pregnancy and choose the appropriate delivery method, as well as help in medical diagnosis and treatment.

Suggested Citation

  • Maria Tzitiridou-Chatzopoulou & Georgia Zournatzidou & Michael Kourakos, 2024. "Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland," IJERPH, MDPI, vol. 21(7), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:7:p:841-:d:1423819
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    References listed on IDEAS

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    1. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    2. Arkadiusz Wiśniowski & Peter Smith & Jakub Bijak & James Raymer & Jonathan Forster, 2015. "Bayesian Population Forecasting: Extending the Lee-Carter Method," Demography, Springer;Population Association of America (PAA), vol. 52(3), pages 1035-1059, June.
    3. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    4. Leontine Alkema & Adrian Raftery & Patrick Gerland & Samuel Clark & François Pelletier & Thomas Buettner & Gerhard Heilig, 2011. "Probabilistic Projections of the Total Fertility Rate for All Countries," Demography, Springer;Population Association of America (PAA), vol. 48(3), pages 815-839, August.
    5. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    6. Nicoletta Balbo & Francesco C. Billari & Melinda Mills, 2013. "Fertility in Advanced Societies: A Review of Research," European Journal of Population, Springer;European Association for Population Studies, vol. 29(1), pages 1-38, February.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Barro, Robert J. & Lee, Jong-Wha, 2015. "Education Matters: Global Schooling Gains from the 19th to the 21st Century," OUP Catalogue, Oxford University Press, number 9780199379231.
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    Cited by:

    1. Rodney P. Jones, 2025. "Capacity Planning (Capital, Staff and Costs) of Inpatient Maternity Services: Pitfalls for the Unwary," IJERPH, MDPI, vol. 22(1), pages 1-47, January.

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