Preeclampsia prediction via machine learning: a systematic literature review

Basit Öğe Kaydı
dc.contributor.authorOzcan, Mert
dc.contributor.authorPeker, Serhat
dc.date.accessioned2025-03-20T09:51:03Z
dc.date.available2025-03-20T09:51:03Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractPreeclampsia, a life-threatening condition in late pregnancy, has unclear causes and risk factors. Machine learning (ML) offers a promising approach for early prediction. This systematic review analyzes state-of-the-art studies on preeclampsia prediction using ML approaches. We reviewed articles published between January 1 2013 and December 31 2023, from Google Scholar and PubMed. Of 183 identified studies, 35 were selected based on inclusion and exclusion criteria. Our findings reveal that key predictive features commonly used in machine learning models include age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure. In contrast, factors such as medications, genetic data, and clinical imaging were considered less frequently. Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Na & iuml;ve Bayes were the most commonly used algorithms. Most studies were conducted in China and the USA, indicating geographic concentration. The field has seen a notable rise in research, especially in the past two years, though many studies rely on small datasets from single hospitals. This review highlights the need for more diverse and comprehensive research to enhance early detection and management of preeclampsia.
dc.identifier.doi10.1080/20476965.2024.2435845
dc.identifier.issn2047-6965
dc.identifier.issn2047-6973
dc.identifier.scopus2-s2.0-85211335352
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1080/20476965.2024.2435845
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2385
dc.identifier.wosWOS:001372945400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofHealth Systems
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectPreeclampsia
dc.subjectartificial intelligence
dc.subjectmachine learning
dc.subjectdeep learning
dc.subjectpregnancy
dc.titlePreeclampsia prediction via machine learning: a systematic literature review
dc.typeReview

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu