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

(1) Background: Prenatal care providers face a continuous challenge in screening for intrauterine growth restriction (IUGR) and preeclampsia (PE). In this study, we aimed to assess and compare the predictive accuracy of four machine learning algorithms in predicting the occurrence of PE, IUGR, and t...

Full description

Saved in:
Bibliographic Details
Published in:Diagnostics (Basel) 2024-02, Vol.14 (4), p.453
Main Authors: Vasilache, Ingrid-Andrada, Scripcariu, Ioana-Sadyie, Doroftei, Bogdan, Bernad, Robert Leonard, Cărăuleanu, Alexandru, Socolov, Demetra, Melinte-Popescu, Alina-Sînziana, Vicoveanu, Petronela, Harabor, Valeriu, Mihalceanu, Elena, Melinte-Popescu, Marian, Harabor, Anamaria, Bernad, Elena, Nemescu, Dragos
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Autoimmune diseases
Biomarkers
Body mass index
Data mining
Diabetes
Disease prevention
Edema
Family medical history
Gestational age
Growth
Gynecology
Hypertension
intrauterine growth restriction
Ischemia
Kidney diseases
Machine learning
Obstetrics
prediction
Preeclampsia
Pregnancy
Pregnant women
Premature birth
Risk factors
screening
Ultrasonic imaging
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:(1) Background: Prenatal care providers face a continuous challenge in screening for intrauterine growth restriction (IUGR) and preeclampsia (PE). In this study, we aimed to assess and compare the predictive accuracy of four machine learning algorithms in predicting the occurrence of PE, IUGR, and their associations in a group of singleton pregnancies; (2) Methods: This observational prospective study included 210 singleton pregnancies that underwent first trimester screenings at our institution. We computed the predictive performance of four machine learning-based methods, namely decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), by incorporating clinical and paraclinical data; (3) Results: The RF algorithm showed superior performance for the prediction of PE (accuracy: 96.3%), IUGR (accuracy: 95.9%), and its subtypes (early onset IUGR, accuracy: 96.2%, and late-onset IUGR, accuracy: 95.2%), as well as their association (accuracy: 95.1%). Both SVM and NB similarly predicted IUGR (accuracy: 95.3%), while SVM outperformed NB (accuracy: 95.8 vs. 94.7%) in predicting PE; (4) Conclusions: The integration of machine learning-based algorithms in the first-trimester screening of PE and IUGR could improve the overall detection rate of these disorders, but this hypothesis should be confirmed in larger cohorts of pregnant patients from various geographical areas.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14040453