Machine learning use in early ovarian cancer detection

被引：0

作者：

Emmanuel Kokori ^{[1
]}

Nicholas Aderinto ^{[2
]}

Gbolahan Olatunji ^{[1
]}

Israel Charles Abraham ^{[1
]}

Rosemary Komolafe ^{[1
]}

Bonaventure Ukoaka ^{[3
]}

Owolabi Samuel ^{[4
]}

Chidiogo Ezenwoba ^{[5
]}

Ebuka Kennedy Anyachebelu ^{[6
]}

机构：

[1] University of Ilorin,Department of Medicine and Surgery

[2] Ladoke Akintola University of Technology,Department of Medicine and Surgery

[3] Asokoro District Hospital,Department of Internal Medicine

[4] Lagos State Health Service Commission,Department of Medicine

[5] University of Benin Teaching Hospital,Department of Obstetrics and Gynaecology

[6] Lagos State University Teaching Hospital,State Clinical Mentor, Ministry of Health

来源：

Discover Medicine | / 2卷 / 1期

关键词：

Ovarian cancer; Early detection; Machine learning; Machine learning algorithms;

D O I：

10.1007/s44337-025-00260-6

中图分类号：

学科分类号：

摘要：

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.

引用