Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors

Muhammad Mursil; Hatem A Rashwan; Adnan Khalid; Pere Cavallé-Busquets; Luis Santos-Calderon; Michelle M Murphy; Domenec Puig

doi:10.1016/j.jbi.2025.104838

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors

J Biomed Inform. 2025 Jun:166:104838. doi: 10.1016/j.jbi.2025.104838. Epub 2025 May 6.

Authors

Muhammad Mursil¹, Hatem A Rashwan², Adnan Khalid², Pere Cavallé-Busquets³, Luis Santos-Calderon⁴, Michelle M Murphy⁴, Domenec Puig²

Affiliations

¹ Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, 43007, Tarragona, Spain. Electronic address: muhammad.mursil@urv.cat.
² Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, 43007, Tarragona, Spain.
³ Unit of Obstetrics & Gynaecology, University Hospital Sant Joan, Reus, IISPV, CIBERObn ISCII, 43201, Tarragona, Spain.
⁴ Faculty of Medicine and Health Sciences, IISPV, Universitat Rovira i Virgili, Reus, CIBERObn ISCIII, 43201, Tarragona, Spain.

PMID: 40339967
DOI: 10.1016/j.jbi.2025.104838

Abstract

Background: Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional prediction methods depend heavily on ultrasonography, which does not consider risk factors affecting birth weight (BW).

Objective: This study introduces a robust deep neural network for a clinical decision-support system designed to early predict neonatal BW, using data available during early pregnancy, with enhanced precision. This innovative system incorporates a comprehensive array of maternal factors, placing particular emphasis on nutritional elements alongside physiological and lifestyle variables.

Methods: We employed and validated various traditional machine learning models as well as an interpretable deep learning model using the TabNet architecture, noted for its proficient handling of tabular data and high level of interpretability. The efficacy of these models was evaluated against extensive datasets that encompass a broad spectrum of maternal health indicators.

Results: The TabNet model exhibited outstanding predictive capabilities, achieving an accuracy of 96% and an area under the curve (AUC) of 0.96. Significantly, maternal vitamin B12 and folate status emerged as pivotal predictors of BW, emphasizing the crucial role of nutritional factors in influencing neonatal health outcomes.

Conclusions: Our results demonstrate the substantial benefits of integrating multimodal maternal factors into predictive models for neonatal BW, markedly enhancing the precision over traditional AI methods. The developed decision-support system not only has a possible application in prenatal care but also provides actionable insights that can be leveraged to mitigate the risks associated with LBW, thereby improving clinical decision-making processes and outcomes.

Keywords: Birth weight prediction; Deep learning; Explainable AI; Fetal development; Machine learning; Maternal factors; Maternal health.

MeSH terms

Birth Weight*
Decision Support Systems, Clinical
Deep Learning*
Female
Humans
Infant, Low Birth Weight
Infant, Newborn
Machine Learning
Neural Networks, Computer*
Pregnancy
Risk Factors