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Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospe...

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Autores principales: Melinte-Popescu, Alina-Sinziana, Vasilache, Ingrid-Andrada, Socolov, Demetra, Melinte-Popescu, Marian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865606/
https://www.ncbi.nlm.nih.gov/pubmed/36675347
http://dx.doi.org/10.3390/jcm12020418
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author Melinte-Popescu, Alina-Sinziana
Vasilache, Ingrid-Andrada
Socolov, Demetra
Melinte-Popescu, Marian
author_facet Melinte-Popescu, Alina-Sinziana
Vasilache, Ingrid-Andrada
Socolov, Demetra
Melinte-Popescu, Marian
author_sort Melinte-Popescu, Alina-Sinziana
collection PubMed
description (1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients’ clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of pregnancy.
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spelling pubmed-98656062023-01-22 Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study Melinte-Popescu, Alina-Sinziana Vasilache, Ingrid-Andrada Socolov, Demetra Melinte-Popescu, Marian J Clin Med Article (1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients’ clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of pregnancy. MDPI 2023-01-04 /pmc/articles/PMC9865606/ /pubmed/36675347 http://dx.doi.org/10.3390/jcm12020418 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Melinte-Popescu, Alina-Sinziana
Vasilache, Ingrid-Andrada
Socolov, Demetra
Melinte-Popescu, Marian
Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
title Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
title_full Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
title_fullStr Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
title_full_unstemmed Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
title_short Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
title_sort predictive performance of machine learning-based methods for the prediction of preeclampsia—a prospective study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865606/
https://www.ncbi.nlm.nih.gov/pubmed/36675347
http://dx.doi.org/10.3390/jcm12020418
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