A comprehensive investigation into the use of machine learning to forecast ovarian cancer

Ovarian cancer, a deadly disease, affects women worldwide. Early detection improves treatment and survival. This research investigates ovarian cancer prediction using machine learning, a potential technology. Ovarian cancer's history, symptoms, and current diagnosis are covered. Early detection and machine learning's potential to improve diagnosis and therapy are highlighted. Machine learning ovarian cancer prediction research is summarised next. Discussions include logistic regression, decision trees, random forests, and support vector machines. We compare method accuracy and discuss their pros and cons. The study suggests machine learning algorithms could predict ovarian cancer. Model accuracy and AUC values showed strong forecast performance. These findings can improve clinical early diagnosis and treatment outcomes, the scientists say. The paper concludes with the study's drawbacks, including the need for larger datasets and more diverse patient populations. The authors advise using deep learning and genomes and proteomics data to advance the area.