Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.

ABSTRACT: Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-based semi-supervised learning. This is a secondary analysis of a prospective study of healthy pregnant women. To develop the prediction model, we compared the prediction performances across five machine learning methods (semi-supervised learning with both labeled and unlabeled data, semi-supervised learning with labeled data only, logistic regression, support vector machine, and random forest) using three different variable sets: [a] variables from clinical guidelines, [b] selected important vari

SUBMITTER: Lee SM 

PROVIDER: S-EPMC9499925 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature