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- Title
Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study.
- Authors
Melinte-Popescu, Marian; Vasilache, Ingrid-Andrada; Socolov, Demetra; Melinte-Popescu, Alina-Sînziana
- 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.
- Subjects
ROMANIA; MISSISSIPPI; HELLP syndrome; MACHINE learning; K-nearest neighbor classification; LIVER enzymes; RANDOM forest algorithms
- Publication
Diagnostics (2075-4418), 2023, Vol 13, Issue 2, p287
- ISSN
2075-4418
- Publication type
Article
- DOI
10.3390/diagnostics13020287