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

Author

Listed:
  • 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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    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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