Published Paper Details:

Ovarian cancer remains a strong foe in the arena of women's health, ranking as one of the major causes of cancer-related death, especially when not discovered early. The current diagnostic landscape is heavily reliant on a multifaceted approach involving surgical interventions, ancestral lineage assessments, imaging techniques such as ultrasound and CT-Scans, and specialized blood tests such as CA125, all of which aim to differentiate between benign and malignant ovarian tumors. Early identification of ovarian cancer is critical, and developing machine learning tools provide potential prospects. The ability of machine learning to comprehend complicated data and provide accurate forecasts has the potential to revolutionize the diagnosis and therapy of ovarian cancer. Notably, numerous machine learning algorithms, like as Naive Bayes and Simple Regression, have demonstrate and their diagnostic capability in the diagnosis of ovarian cancer, with accuracies of 89.25% and 88.17%, respectively, across multiple repositories. The continuing study's major goal is to investigate and use various machine learning algorithms in the detection of ovarian cancer. This study aims to demonstrate a wide range of machine learning approaches designed for the early and accurate detection of both malignant and benign tumors linked with ovarian cancer. The investigation seeks not only to improve diagnosis accuracy, but also to shorten the procedure, potentially improving the efficacy of early interventions and personalized treatment paths in the field of ovarian cancer management.