Developing a predictive model for stillbirth risk using machine learning algorithms

Mayasee Kuanakewinyoo (), Arthit I Ntarasit (), Jarunee Saelee () and Jirapond Muangprathub ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 4, 1897-1907

Abstract: Stillbirths continue to be a significant global issue, reflecting the quality of obstetric care. In Thailand, stillbirth remains a concern, especially in southern provinces like Pattani. Traditional risk identification focuses on historical obstetric complications, but there is a need for advanced technology to improve early detection and prevention. This study investigates the factors influencing stillbirth risk by analyzing perinatal data from 2018 to 2021 in Pattani Province. A machine learning-based predictive model was developed using various algorithms, including Naïve Bayes, logistic regression, deep learning, decision tree, random forest, and gradient-boosted trees, to handle imbalanced datasets. Performance comparisons of these models were conducted, and the best-performing model was implemented into a web application to provide personalized recommendations to pregnant women. The study highlights the importance of advanced data analytics in stillbirth prevention and offers an innovative, accessible solution for pregnant women to manage and monitor their pregnancies. The system not only improves the prediction of stillbirth risks but also provides an interactive, low-cost platform for delivering real-time suggestions, aiming to reduce stillbirth occurrences and improve maternal health outcomes.

Keywords: Machine learning; Pregnancies; Risk prediction; Stillbirth risk. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijirss.com/index.php/ijirss/article/view/8259/1850 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:4:p:1897-1907:id:8259

Access Statistics for this article

International Journal of Innovative Research and Scientific Studies is currently edited by Natalie Jean

More articles in International Journal of Innovative Research and Scientific Studies from Innovative Research Publishing
Bibliographic data for series maintained by Natalie Jean ().