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

Area of Science:

• Demography
• Public Health
• Machine Learning

Background:

• Total fertility rate is influenced by socioeconomic factors and values.
• Macroeconomic trends can impact fertility short-term, especially at low fertility rates.
• Accurate forecasting of fertility trends is vital for anticipating demographic shifts and policy needs.

Purpose of the Study:

• To forecast monthly births in Scotland using advanced analytical methods.
• To highlight the importance of precise fertility trend prediction for various sectors.
• To demonstrate a machine learning model for clinical decision-making and healthcare management.

Main Methods:

• Analysis of registered births in Scotland.
• Application of non-linear machine learning methods.
• Integration of traditional statistical approaches for forecasting.
• Out-of-sample, one-step-ahead forecasting exercise.

Main Results:

• Machine learning methods proved effective in generating accurate birth predictions.
• The study underscores the efficacy of advanced models in demographic forecasting.
• Demonstrated the utility of machine learning in predicting pregnancy complications and optimizing delivery methods.

Conclusions:

• Machine learning offers a powerful tool for precise monthly birth forecasting.
• Accurate forecasts support informed policy-making for fiscal stability, economic accounts, and environmental planning.
• The developed model has implications for clinical decision-making, pregnancy management, and medical diagnosis.