ArticleDiagnostics (Basel, Switzerland)2023
Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms-Results from a Retrospective Study.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Hypoxia-Inducible Factor 1-Alpha Gene Polymorphisms Impact Risk of Severespectrum Hypertensive Disorders of Pregnancy: A Case-Control Study.Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
- Presenting a prediction model for HELLP syndrome through data mining.BMC medical informatics and decision making · 2025Article
- Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network.Diagnostics (Basel, Switzerland) · 2025Article
- Epigenetic alterations in preeclampsia: a focus on microRNA149 and tetrahydrofolate reductase gene polymorphisms in Egyptian women.Irish journal of medical science · 2024Article
- Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2024Article
- Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms.Medicina (Kaunas, Lithuania) · 2024Article
- Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of Intrauterine Growth Restriction and Preeclampsia Using Machine Learning-Based Algorithms: A Prospective Study.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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