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Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study
(1) Background: HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome is a rare and life-threatening complication of preeclampsia. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HELLP syndrome,...
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/PMC9858219/ https://www.ncbi.nlm.nih.gov/pubmed/36673097 http://dx.doi.org/10.3390/diagnostics13020287 |
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author | Melinte-Popescu, Marian Vasilache, Ingrid-Andrada Socolov, Demetra Melinte-Popescu, Alina-Sînziana |
author_facet | Melinte-Popescu, Marian Vasilache, Ingrid-Andrada Socolov, Demetra Melinte-Popescu, Alina-Sînziana |
author_sort | Melinte-Popescu, Marian |
collection | PubMed |
description | (1) Background: HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome is a rare and life-threatening complication of preeclampsia. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HELLP syndrome, and its subtypes according to the Mississippi classification; (2) Methods: This retrospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between January 2007 and December 2021. The patients’ clinical and paraclinical characteristics were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), k-nearest neighbors (KNN), and random forest (RF), and their predictive performance were assessed; (3) Results: Our results showed that HELLP syndrome was best predicted by RF (accuracy: 89.4%) and NB (accuracy: 86.9%) models, while DT (accuracy: 91%) and KNN (accuracy: 87.1%) models had the highest performance when used to predict class 1 HELLP syndrome. The predictive performance of these models was modest for class 2 and 3 of HELLP syndrome, with accuracies ranging from 65.2% and 83.8%; (4) Conclusions: The machine learning-based models could be useful tools for predicting HELLP syndrome, and its most severe form—class 1. |
format | Online Article Text |
id | pubmed-9858219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98582192023-01-21 Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study Melinte-Popescu, Marian Vasilache, Ingrid-Andrada Socolov, Demetra Melinte-Popescu, Alina-Sînziana Diagnostics (Basel) Article (1) Background: HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome is a rare and life-threatening complication of preeclampsia. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HELLP syndrome, and its subtypes according to the Mississippi classification; (2) Methods: This retrospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between January 2007 and December 2021. The patients’ clinical and paraclinical characteristics were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), k-nearest neighbors (KNN), and random forest (RF), and their predictive performance were assessed; (3) Results: Our results showed that HELLP syndrome was best predicted by RF (accuracy: 89.4%) and NB (accuracy: 86.9%) models, while DT (accuracy: 91%) and KNN (accuracy: 87.1%) models had the highest performance when used to predict class 1 HELLP syndrome. The predictive performance of these models was modest for class 2 and 3 of HELLP syndrome, with accuracies ranging from 65.2% and 83.8%; (4) Conclusions: The machine learning-based models could be useful tools for predicting HELLP syndrome, and its most severe form—class 1. MDPI 2023-01-12 /pmc/articles/PMC9858219/ /pubmed/36673097 http://dx.doi.org/10.3390/diagnostics13020287 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, Marian Vasilache, Ingrid-Andrada Socolov, Demetra Melinte-Popescu, Alina-Sînziana Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study |
title | Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study |
title_full | Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study |
title_fullStr | Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study |
title_full_unstemmed | Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study |
title_short | Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study |
title_sort | prediction of hellp syndrome severity using machine learning algorithms—results from a retrospective study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858219/ https://www.ncbi.nlm.nih.gov/pubmed/36673097 http://dx.doi.org/10.3390/diagnostics13020287 |
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