  Ovarian Cancer (OC) is type of female reproductive malignancy which can be
found among young girls and mostly the women in their fertile or reproductive.
There are few number of cysts are dangerous and may it cause cancer. So, it is
very important to predict and it can be from different types of screening are
used for this detection using Transvaginal Ultrasonography (TVUS) screening. In
this research, we employed an actual datasets called PLCO with TVUS screening
and three machine learning (ML) techniques, respectively Random Forest KNN, and
XGBoost within three target variables. We obtained a best performance from this
algorithms as far as accuracy, recall, f1 score and precision with the
approximations of 99.50%, 99.50%, 99.49% and 99.50% individually. The AUC score
of 99.87%, 98.97% and 99.88% are observed in these Random Forest, KNN and XGB
algorithms .This approach helps assist physicians and suspects in identifying
ovarian risks early on, reducing ovarian malignancy-related complications and
deaths.
