A1 Refereed original research article in a scientific journal
Predicting risk of stillbirth and preterm pregnancies with machine learning
Authors: Koivu Aki, Sairanen Mikko
Publisher: Springer
Publication year: 2020
Journal: Health Information Science and Systems
Journal name in source: Health information science and systems
Journal acronym: Health Inf Sci Syst
Article number: 14
Volume: 8
Issue: 1
Number of pages: 12
ISSN: 2047-2501
eISSN: 2047-2501
DOI: https://doi.org/10.1007/s13755-020-00105-9
Web address : https://link.springer.com/article/10.1007/s13755-020-00105-9
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/46690123
Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel risk models that could be utilized in a clinical setting. A CDC data set of almost sixteen million observations was used conduct feature selection, parameter optimization and verification of proposed models. An additional NYC data set was used for external validation. Algorithms such as logistic regression, artificial neural network and gradient boosting decision tree were used to construct individual classifiers. Ensemble learning strategies of these classifiers were also experimented with. The best performing machine learning models achieved 0.76 AUC for early stillbirth, 0.63 for late stillbirth and 0.64 for preterm birth while using a external NYC test data. The repeatable performance of our models demonstrates robustness that is required in this context. Our proposed novel models provide a solid foundation for risk prediction and could be further improved with the addition of biochemical and/or biophysical markers.
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