Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction

Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction

Background: Machine learning (ML), with advancements in algorithms and computations, is seeing an increased presence in life science research. This study investigated several ML models' efficacy in predicting preterm birth using untargeted metabolomics from serum collected during the third trim...

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Bibliographic Details
Main Authors: Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Preterm birth
Pregnancy
Metabolomics
Machine learning
Predictive models
XGBoost
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037025002752
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