Preeclampsia prediction via machine learning: a systematic literature review.

ABSTRACT Preeclampsia, 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 Naïve 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. Acknowledgments The authors sincerely thank the editors and anonymous reviewers for their thorough review and valuable suggestions to improve this paper. Disclosure statement No potential conflict of interest was reported by the author(s).