Melinte-Popescu Marian, Vasilache Ingrid-Andrada, Socolov Demetra, Melinte-Popescu Alina-Sînziana
Department of Internal Medicine, Faculty of Medicine and Biological Sciences, 'Ștefan cel Mare' University, 720229 Suceava, Romania.
Department of Obstetrics and Gynecology, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania.
Diagnostics (Basel). 2023 Jan 12;13(2):287. doi: 10.3390/diagnostics13020287.
(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.
（1）背景：HELLP（溶血、肝酶升高和血小板减少）综合征是先兆子痫一种罕见且危及生命的并发症。本研究的目的是评估和比较四种基于机器学习的模型对HELLP综合征及其根据密西西比分类法划分的亚型的预测性能；（2）方法：这项回顾性病例对照研究评估了2007年1月至2021年12月期间在罗马尼亚一家三级妇产医院就诊的女性所发生的妊娠情况。患者的临床和辅助检查特征被纳入四种基于机器学习的模型：决策树（DT）、朴素贝叶斯（NB）、k近邻（KNN）和随机森林（RF），并评估它们的预测性能；（3）结果：我们的结果表明，RF模型（准确率：89.4%）和NB模型（准确率：86.9%）对HELLP综合征的预测效果最佳，而DT模型（准确率：91%）和KNN模型（准确率：87.1%）在用于预测1级HELLP综合征时性能最高。这些模型对2级和3级HELLP综合征的预测性能一般，准确率在65.2%至83.8%之间；（4）结论：基于机器学习的模型可能是预测HELLP综合征及其最严重形式——1级的有用工具。
