Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospe...

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Bibliographic Details
Published in:Journal of clinical medicine 2023-01, Vol.12 (2), p.418
Main Authors: Melinte-Popescu, Alina-Sinziana, Vasilache, Ingrid-Andrada, Socolov, Demetra, Melinte-Popescu, Marian
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biomarkers
Blood pressure
Clinical medicine
Creatinine
Decision trees
Family medical history
Gestational age
Hyperglycemia
Hypertension
Kidney diseases
Machine learning
Preeclampsia
Pregnancy
Proteins
Regression analysis
Risk factors
Support vector machines
Thrombocytopenia
Ultrasonic imaging
Womens health
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Summary:(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients' clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of pregnancy.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm12020418