Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015.

ABSTRACT: Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal socio-demographics and medical cond

SUBMITTER: Wong K 

PROVIDER: S-EPMC9646808 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature