Categorization of used birth weights datasets.

<div><p>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 lo...

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Bibliographic Details
Main Author:	Ali Nawaz (10966344) (author)
Other Authors:	Amir Ahmad (17521641) (author), Shehroz S. Khan (15245525) (author), Mohammad Mehedy Masud (20642195) (author), Nadirah Ghenimi (17705890) (author), Luai A. Ahmed (10137639) (author)
Published:	2025
Subjects:	Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified various prenatal features receiver operating characteristics proposed interpretation methodology extreme birth weights detect anomalies indicative early intervention strategies extreme lbw detection based outlier detection performance using area low lbw cases anomaly detection models efficient interpretable framework interpretable framework early identification anomaly class unsupervised detection unsupervised low unsupervised learning traditional approaches study presents study explores significant neonatal risk pregnancies reveals promise relative importance recall curve providing insights preliminary insights particularly valuable mortality risks maternal morbidity labeled data experiments demonstrated data labeling clinician experts
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Categorization of used birth weights datasets.

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