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Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study
- Source :
- Diagnostics; Volume 13; Issue 2; Pages: 287
- Publication Year :
- 2023
- Publisher :
- Multidisciplinary Digital Publishing Institute, 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.
- Subjects :
- Clinical Biochemistry
HELLP syndrome
severity
prediction
machine learning
Subjects
Details
- Language :
- English
- ISSN :
- 20754418
- Database :
- OpenAIRE
- Journal :
- Diagnostics; Volume 13; Issue 2; Pages: 287
- Accession number :
- edsair.doi.dedup.....6e7ebfdb3548cfd9b26354f0d3aed7ad
- Full Text :
- https://doi.org/10.3390/diagnostics13020287