Construction and Validation of a Model for Predicting Fear of Childbirth: A Cross-Sectional Population Study via Machine Learning

Gravidity, history of adverse pregnancy outcomes, history of cesarean section, planned pregnancy, payment method, and SSRS score are risk factors for FOC in late-pregnancy women. The risk predictive model established in this study has a high clinical reference value.

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Publication Details

Journal
International Journal of Women s Health
Published
2025-02-01
DOI
https://doi.org/10.2147/ijwh.s508153
Citations
2
Primary Topic
Maternal and Perinatal Health Interventions
Type
article
Field-Weighted Citation Impact
4.11

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article

Construction and Validation of a Model for Predicting Fear of Childbirth: A Cross-Sectional Population Study via Machine Learning

Lan Zheng, Shu‐Guang Zhou, Kai Chen, Miaomiao Zhu et al.
2 citations
International Journal of Women s Health
Maternal and Perinatal Health Interventions
4.11
article

Construction and Validation of a Model for Predicting Fear of Childbirth: A Cross-Sectional Population Study via Machine Learning

Lan Zheng, Shu‐Guang Zhou, Kai Chen, Miaomiao Zhu, Lindi Jiang, Hui Cheng, Zhi-Lin Zhang, Jingjing Gu
article en
2 citations

Abstract

Gravidity, history of adverse pregnancy outcomes, history of cesarean section, planned pregnancy, payment method, and SSRS score are risk factors for FOC in late-pregnancy women. The risk predictive model established in this study has a high clinical reference value.

International Journal of Women s HealthVol. Volume 17
Anhui Medical University (CN)
National Health Commission of the People's Republic of China
Gender equality
Openalex Percentile: Top 9%
Maternal and Perinatal Health Interventions
4.11
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