Ovarian Cancer Prediction from Ovarian Cysts Based on TVUS Using Machine Learning Algorithms
This work addresses early detection of ovarian cancer for women, particularly those with ovarian cysts, but it is incremental as it applies existing ML methods to a specific medical dataset.
The study tackled ovarian cancer prediction from ovarian cysts using TVUS screening and machine learning, achieving high performance metrics such as 99.50% accuracy and up to 99.88% AUC score with algorithms like Random Forest and XGBoost.
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.