Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE.

ABSTRACT: Very Preterm birth (vPTB) and extreme Preterm birth (xPTB) are the major concerns in maternal and child healthcare and are associated with increased morbidity and mortality. Machine learning methods have traditionally been used to predict preterm births (vPTB and xPTB). However, most medical datasets, including preterm births, are imbalanced in class distribution. Although data-balancing techniques can be employed, complications due to the limited sample size of the minority class are frequently encountered, leading to inconsistent results. This study adopted a novel approach by employing one-class classification (OCC) in conjunction with several strategies to predict instances of vPTB and xPTB within an Emirati pregnant population. We used a well-curated dataset acquired during the first

SUBMITTER: Ahmad A 

PROVIDER: S-EPMC12764955 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature