ArticleScientific reports2023
Partner relationships, hopelessness, and health status strongly predict maternal well-being: an approach using light gradient boosting machine.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- Machine learning-driven prioritization and experimental validation of traditional Chinese medicine-derived STING-inhibitory candidates.Molecular diversity · 2026Article
- Using Machine Learning to Analyze the Predictors of Life Satisfaction: Focus on Lifestyle Attitudes and Psychological Factors.International journal of methods in psychiatric research · 2026Article
- Decline in and recovery of fertility rates after COVID-19-related state of emergency in Japan.BMJ open · 2024Article
- MLASM: Machine learning based prediction of anticancer small molecules.Molecular diversity · 2024Article
- Construction of IRAK4 inhibitor activity prediction model based on machine learning.Molecular diversity · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 pregnant women in 2021, with an accuracy of 88%. These factors were also significant risk factors for subjective well-being in the regression analysis adjusted for maternal age, region, parity, education level, and the presence of mental illness. Mediation analysis, with "hopelessness" as the mediator, showed that both "partner help" and "health status" directly and indirectly affected the outcome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.