AUTHOR=Li Taishun , Xu Mingyang , Wang Yuan , Wang Ya , Tang Huirong , Duan Honglei , Zhao Guangfeng , Zheng Mingming , Hu Yali 

TITLE=Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2024.1345573

DOI=10.3389/fendo.2024.1345573

ISSN=1664-2392

ABSTRACT=Introduction: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 +6 weeks' gestation, providing an effective tool for early screening