Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.

ABSTRACT: Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithm

SUBMITTER: Khan W 

PROVIDER: S-EPMC10752564 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature