Prediction of Breast Cancer Using Ensemble Learning and Boosting Techniques
S Balasubramaniam, M Arishma, Satheesh Kumar K · 2024
An extremely serious illness, breast cancer, is a pathological state characterized by uncontrolled proliferation of cells in the breast. Failure to address the issue could potentially result in death going forward. Therefore, it is crucial to accurately forecast breast cancer incidence in an individual and promptly administer treatment during the most favourable stage. This work utilized machine learning techniques to forecast the occurrence of breast cancer. This study made use of a dataset that was sourced from Kaggle. In order to find the best machine learning approach, their effectiveness was tested. In addition to supervised learning algorithms such as Random Forest, SVM, and Naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis, ensemble learning approaches utilized. Out of the several machine learning models used, the Ensemble learning model XGBoost shown superior performance compared to the other models. This study's data analysis leads to the conclusion that XGBoost is the best model.