Ovarian Cancer Detection and Diagnosis Using Machine Learning Methodology

Ovarian cancer is a formidable adversary in the realm of oncology, characterized by late-stage diagnosis and limited treatment options. Early detection and precise diagnosis are pivotal in mitigating its impact. This paper delves into the realm of machine learning methodologies, specifically focusing on the utilization of Support Vector Machines (SVM) for ovarian cancer detection and diagnosis. We embark on a comprehensive investigation, placing SVM in direct comparison with the widely acclaimed XG Boost algorithm. Through meticulous data collection and preprocessing, we meticulously curate a robust dataset. Our methodology entails training SVM and XG Boost models, and the Support Vector Machine (SVM) demonstrates superior performance in ovarian cancer detection, achieving an outstanding accuracy of 98.08% more over than XG boost (89%).