Partner relationships, hopelessness, and health status strongly predict maternal well-being: an approach using light gradient boosting machine.

ABSTRACT: No recent study has explicitly focused on predicting the well-being of pregnant women. This study used data from an extensive online survey in Japan to examine the predictors of the subjective well-being of pregnant women. We developed and validated a light Gradient Boosting Machine (lightGBM) model using data from 400 pregnant women in 2020 to identify three factors that predict subjective well-being. The results confirmed that the model could predict subjective well-being in pregnant women with 84% accuracy. New variables that contributed significantly to this prediction were "partner help", "hopelessness," and "health status". A new lightGBM model was built with these three factors, trained and validated using data from 400 pregnant women in 2020, and predicted using data from 1791 preg

SUBMITTER: Ooba H 

PROVIDER: S-EPMC10562477 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature