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.
Keywords: predictability, birth rate data, machine learning, fertility rate, demographic challenges
Author Biographies
Maria Tzitiridou-Chatzopoulou, School of Healthcare Sciences, Midwifery Department, University of Western Macedonia, 50100 Kozani, Greece
Conceptualization, Software, Validation, Writing – original draft
Georgia Zournatzidou, Department of Business Administration, University of Western Macedonia, 50100 Kozani, Greece; [email protected]
Methodology, Formal analysis, Resources, Data curation, Writing – original draft
Michael Kourakos, School of Healthcare Sciences, Department of Nursing, University of Ioannina, 45500 Ioannina, Greece; [email protected]
Software, Investigation, Writing – original draft, Supervision, Project administration
Click on "Archives" to access the full archive of scientific preprints. You may use the categories and the search functionality to find select preprints you're interested in.
