{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T11:20:12Z","timestamp":1792495212055,"version":"4.5.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>(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\u2019 clinical and paraclinical characteristics were included in four machine learning-based models: decision tree (DT), na\u00efve 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\u2014class 1.<\/jats:p>","DOI":"10.3390\/diagnostics13020287","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T05:03:03Z","timestamp":1673499783000},"page":"287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms\u2014Results from a Retrospective Study"],"prefix":"10.3390","volume":"13","author":[{"given":"Marian","family":"Melinte-Popescu","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine and Biological Sciences, \u2018\u0218tefan cel Mare\u2019 University, 720229 Suceava, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4718-392X","authenticated-orcid":false,"given":"Ingrid-Andrada","family":"Vasilache","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, \u2018Grigore T. Popa\u2019 University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0979-0200","authenticated-orcid":false,"given":"Demetra","family":"Socolov","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, \u2018Grigore T. Popa\u2019 University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alina-S\u00eenziana","family":"Melinte-Popescu","sequence":"additional","affiliation":[{"name":"Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, \u2018\u0218tefan cel Mare\u2019 University, 720229 Suceava, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0002-9378(16)32330-4","article-title":"Syndrome of hemolysis, elevated liver enzymes, and low platelet count: A severe consequence of hypertension in pregnancy","volume":"142","author":"Weinstein","year":"1982","journal-title":"Am. J. Obs. 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