Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort.

ABSTRACT: BACKGROUND:Preeclampsia and intrauterine growth restriction are placental dysfunction-related disorders (PDDs) that require a referral decision be made within a certain time period. An appropriate prediction model should be developed for these diseases. However, previous models did not demonstrate robust performances and/or they were developed from datasets with highly imbalanced classes. OBJECTIVE:In this study, we developed a predictive model of PDDs by machine learning that uses features at 24-37 weeks' gestation, including maternal characteristics, uterine artery (UtA) Doppler measures, soluble fms-like tyrosine kinase receptor-1 (sFlt-1), and placental growth factor (PlGF). METHODS:A public dataset was taken from a prospective cohort study that included pregnant women with PDDs (66/95

SUBMITTER: Sufriyana H 

PROVIDER: S-EPMC7265111 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature