Machine Learning Algorithms for HELLP Syndrome Prediction: An Approach for early Detection
Keywords:
Hellp Syndrome, Machine Learning, Predictive Models, Making Decisions, Pregnancy complications.Abstract
Machine learning (ML) techniques offer promising avenues for early prediction of HELLP syndrome in pregnancies, a condition associated with significant maternal and fetal morbidity. The main objective of this study is to propose a predictive model for HELLP syndrome, an obstetric syndrome, and to identify the most influential predictors for HELLP syndrome. For this purpose, we used data collected from the medical records of 266 pregnant women between 28 and 38 weeks of gestation. The variables studied are epidemiological, diagnostic, therapeutic, and evolutionary. The study sample included 49 women with Preeclampsia +Hellp Syndrome (PE+HELLP) and, 217 women with Preeclampsia and without Hellp (PE-HELLP). The proposed approach demonstrates robust performance in identifying pregnancies at risk of HELLP syndrome development through meticulous data preprocessing, feature selection, and model training. Validation on independent datasets underscores the model's generalizability and real-world applicability. Ethical considerations regarding patient privacy and algorithmic transparency are addressed, emphasizing the importance of responsible AI deployment in clinical settings. Our findings highlight the potential of ML-driven approaches to revolutionize prenatal care, enabling timely interventions and improved maternal-fetal health outcomes.
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