A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia.

ABSTRACT: Objective: Preeclampsia affects 2-8% of women and doubles the risk of cardiovascular disease in women after preeclampsia. This study aimed to develop a model based on machine learning to predict postpartum cardiovascular risk in preeclamptic women. Methods: Collecting demographic characteristics and clinical serum markers associated with preeclampsia during pregnancy of 907 preeclamptic women retrospectively, we predicted the cardiovascular risk (ischemic heart disease, ischemic cerebrovascular disease, peripheral vascular disease, chronic kidney disease, metabolic system disease or arterial hypertension). The study samples were divided into training sets and test sets randomly in the ratio of 8:2. The prediction model was developed by 5 different machine learning algorithms,

SUBMITTER: Wang G 

PROVIDER: S-EPMC8578855 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature