Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning-Based Prediction of Intrauterine Growth Restriction and Preeclampsia: A Prospective Study

Ioana Sadiye Scripcariu
,
Ingrid Andrada Vasilache
* ORCID logo ,
Ioana Pavaleanu
* ,
Bogdan Doroftei
ORCID logo ,
Alexandru Cărăuleanu
,
Demetra Socolov
ORCID logo ,
Alina-Sinziana Melinte-Popescu
,
Petronela Vicoveanu
,
Valeriu Harabor
,
Elena Mihalceanu
,
Marian Melinte-Popescu
,
Anamaria Harabor
,
Mariana Stuparu-Cretu
ORCID logo ,
Dragos Nemescu
ORCID logo
Version 1 : Received: 23 November 2023 / Approved: 23 November 2023 / Online: 23 November 2023 (08:45:26 CET)

How to cite: Scripcariu, I.S.; Vasilache, I.A.; Pavaleanu, I.; Doroftei, B.; Cărăuleanu, A.; Socolov, D.; Melinte-Popescu, A.; Vicoveanu, P.; Harabor, V.; Mihalceanu, E.; Melinte-Popescu, M.; Harabor, A.; Stuparu-Cretu, M.; Nemescu, D. Machine Learning-Based Prediction of Intrauterine Growth Restriction and Preeclampsia: A Prospective Study. Preprints 2023, 2023111493. https://doi.org/10.20944/preprints202311.1493.v1 Scripcariu, I.S.; Vasilache, I.A.; Pavaleanu, I.; Doroftei, B.; Cărăuleanu, A.; Socolov, D.; Melinte-Popescu, A.; Vicoveanu, P.; Harabor, V.; Mihalceanu, E.; Melinte-Popescu, M.; Harabor, A.; Stuparu-Cretu, M.; Nemescu, D. Machine Learning-Based Prediction of Intrauterine Growth Restriction and Preeclampsia: A Prospective Study. Preprints 2023, 2023111493. https://doi.org/10.20944/preprints202311.1493.v1

Abstract

1) Background: The screening of preeclampsia (PE) and intrauterine growth restriction (IUGR) represents a constant challenge for obstetricians. The aim of this study was to determine and compare the predictive performance of 4 machine learning-based algorithms for the prediction of PE, IUGR, and their association in a cohort of singleton pregnancies; (2) Methods This prospective study was conducted at a tertiary maternity hospital in Romania, and included 210 pregnancies that underwent first trimester screening. We included clinical and paraclinical data into 4 machine learning-based algorithms decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and calculated their predictive performance; (3) Results: RF performed the best when used to predict PE, IUGR, and its subtypes, as well as the association between PE and IUGR. The overall predictive performance of DT for all these disorders was inferior to RF, NB, and SVM. Both SVM and NB had similar accuracy for the prediction of PE, while NB performed better than SVM for the prediction of IUGR; (4) Conclusions: Machine-learning-based algorithms could be useful for the prediction of ischemic placental disease and need to be validated on large cohorts of patients.

Keywords

machine learning; preeclampsia; intrauterine growth restriction; prediction; screening

Subject

Medicine and Pharmacology, Obstetrics and Gynaecology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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