Effect of the random forests with recursive feature elimination for breast cancer classification
Y. Ono, Yoshihiro Mitani · 2021
A breast cancer is the most dangerous disease of the death cause among aged 40–55 women. We need a computer aided diagnosis system for breast cancer classification. In the previous study, the random forests was reported to be one of promising calssifiers for classifying breast cancers. This paper presents the effect of the random forests with recursive feature elimination for breast cancer classification, compered to the state of the art classification techniques, such as XGBosst and LGBM.