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,...

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Bibliographic Details
Published in:Diagnostics (Basel) 2023-01, Vol.13 (2), p.287
Main Authors: Melinte-Popescu, Marian, Vasilache, Ingrid-Andrada, Socolov, Demetra, Melinte-Popescu, Alina-Sînziana
Format: Article
Language:English
Subjects:
Algorithms
Biomarkers
Blood platelets
Blood pressure
Body mass index
Diabetes
Edema
Feature selection
HELLP syndrome
Hypertension
Informed consent
Kidney diseases
Machine learning
Maternal mortality
prediction
Preeclampsia
Pregnancy
Risk factors
severity
Statistical analysis
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Summary:(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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13020287