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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9865606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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