Multidimensional influencing factors of childbearing intention of women in Dongguan City and prediction model based on machine learning

The purpose of this study was to explore the multi-dimensional influencing factors of fertility intention of women of childbearing age in Dongguan City, and to construct a prediction model based on machine learning. The data was collected through questionnaires and analyses using descriptive statistics, correlation analysis and a variety of machine learning algorithms. The study found that personal characteristics, family environment, socioeconomic and policy factors all affect fertility intention. The machine learning prediction model based on these factors shows high accuracy and stability, which can provide reference for the relevant departments to formulate fertility policies. The results are of great significance for understanding the reproductive behaviors of women of childbearing age in Dongguan City and predicting the future fertility trend.