Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.

ABSTRACT: Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on cl

SUBMITTER: Malacova E 

PROVIDER: S-EPMC7093523 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature