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

ABSTRACT:

Objective

To predict preterm birth in nulliparous women using logistic regression and machine learning.

Design

Population-based retrospective cohort.

Participants

Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014.

Methods

We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" samp

SUBMITTER: Arabi Belaghi R 

PROVIDER: S-EPMC8244906 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature