Machine learning use in early ovarian cancer detection

Abstract

Ovarian cancer remains a significant public health challenge due to the difficulty of early detection. This review explores the promising potential of machine learning (ML) algorithms in this domain. We analyze studies that investigate the application of ML for early ovarian cancer detection. The review highlights the effectiveness of various ML algorithms, including support vector machines (SVMs), random forests, and XGBoost, in achieving high diagnostic accuracy. Studies exploring diverse data sources, such as blood tests, genetic data, and medical images, demonstrate the versatility of ML for ovarian cancer detection. Notably, the ability to tailor ML models to specific risk groups and disease stages is a crucial advancement with the potential to further improve diagnostic accuracy. However, challenges related to data quality, standardization, and ethical considerations require attention. The review concludes by emphasizing the need for future research focused on refining existing models, exploring deep learning techniques, and incorporating multi-omics data. Additionally, addressing data quality and bias is essential for ensuring the equitable application of ML-based tools. Overall, this review underscores the transformative potential of machine learning in enhancing the accuracy and effectiveness of early ovarian cancer detection, ultimately leading to improved patient outcomes.