Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.

ABSTRACT: To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). The cohort included all multiparous women who delivered a singleton birth at 20-42 weeks' gestation in an Ontario hospital between April 1, 2012 and March 31, 2014. The primary outcome was PTB < 37 weeks, with spontaneous PTB analyzed as a secondary outcome. Stepwise logistic regression and the Boruta machine learning were used to select the important variables during the first and second trimester. For building prediction models, the whole data set were divided for the two independent parts: two-third for training the classifiers (Logistic regre

SUBMITTER: Belaghi RA 

PROVIDER: S-EPMC11415355 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature