Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms-Results from a Retrospective Study.
Diagnostics (Basel)
; 13(2)2023 Jan 12.
Article
em En
| MEDLINE
| ID: mdl-36673097
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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Base de dados:
MEDLINE
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article