An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection.

ABSTRACT: Detecting low birth weight is crucial for early identification of at-risk pregnancies which are associated with significant neonatal and maternal morbidity and mortality risks. This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios. This method is particularly valuable in contexts where labeled data are scarce or labels for the anomaly class are not available, allowing for preliminary insights and detection that can inform further data labeling and more focused supervised learning efforts. We employed fourteen different anom

SUBMITTER: Nawaz A 

PROVIDER: S-EPMC11781751 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature