Early detection of ovarian cancer

Ovarian cancer is one of the leading causes of cancer-related deaths among women, primarily due to late- stage diagnosis and the absence of early symptoms. This study aims to enhance diagnostic accuracy using clinical data, biomarkers, and ultrasound imaging by leveraging machine learning techniques, including decision trees, k-nearest neighbors (KNN), and random forest classifiers. Our proposed method achieves high diagnostic accuracy, with the random forest model reaching 93%, followed by decision tree and KNN under specific parameter settings. This non- invasive and scalable approach minimizes false negatives and enhances diagnostic confidence by identifying subtle patterns in medical imaging and clinical data. Our solution offers a cost-effective alternative suitable for diverse clinical settings, particularly in resource-constrained environments. By integrating machine learning into clinical workflows, this research advances AI-driven diagnostics in oncology, laying the foundation for improved early detection of ovarian cancer. The model evaluation revealed that the random forest achieved an accuracy of 93%, decision tree attained 88.57%, and KNN reached 85.71%, demonstrating the effectiveness of our approach in early-stage ovarian cancer detection.