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Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study

Authors :
Marian Melinte-Popescu
Ingrid-Andrada Vasilache
Demetra Socolov
Alina-Sînziana Melinte-Popescu
Source :
Diagnostics, Vol 13, Iss 2, p 287 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

(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.

Details

Language :
English
ISSN :
20754418
Volume :
13
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.0e93f9b1ad147dfbabee98ca08e58f8
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics13020287