Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease.

ABSTRACT: Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k-nearest neighbor, naïve Bayes, and multilayer pe

SUBMITTER: Chu R 

PROVIDER: S-EPMC7660735 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature