Revolutionizing reproductive health: a performance review of machine learning algorithms in clinical infertility and maternal care

Abstract

Infertility and maternal health complications represent significant global health challenges. The integration of machine learning (ML) algorithms holds immense promise for improving clinical decision-making, risk stratification, and patient management in these areas. This review explores the pivotal role of ML in identifying maternal health risk factors contributing to infertility and optimizing reproductive outcomes. We critically examine the performance and application of various ML algorithms, including random forest (RF), support vector machine (SVM), XGBoost, convolutional neural networks (CNNs), and logistic regression (LR), as they are deployed to enhance predictive modeling, diagnosis, and personalized care in reproductive medicine. Our analysis synthesizes their primary clinical applications and typical performance metrics across key areas such as in vitro fertilization (IVF) success prediction, early disease diagnosis (e.g., polycystic ovary syndrome (PCOS), preeclampsia, endometriosis), and comprehensive maternal risk assessment. We highlight that while traditional models like LR offer valuable interpretability, advanced hybrid and multi-modal approaches are increasingly demonstrating superior predictive power by effectively integrating diverse data types, from clinical records to medical images. The report concludes by emphasizing the transformative potential of ML in improving prognostic counseling and resource allocation within reproductive health. However, it also underscores critical challenges that must be addressed for broader clinical adoption, including data standardization, model generalizability across varied populations, and the development of explainable AI to foster trust and facilitate seamless clinical integration.

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