Bagging Techniques to Reduce Misclassification of Breast Cancer Prediction Base on Gradient Boosted Trees (GBT) Algorithm
Teti Desyani, Y Kasmayanti, Aries Saifudin, Yulianti Yulianti · Journal of Physics Conference Series · 2020
Abstract Breast cancer is a malignant tumour that grows in breast cells and has a risk of death. Breast cancer has levels ranging from stage 0 to stage 4. The higher the stage of breast cancer, the higher the risk of death and is difficult to treat. The application of machine learning algorithms has been proposed to help predict breast cancer. Predictions made by classifying patients tend to have breast cancer or not. This research proposes to implement bagging techniques to reduce misclassification in the Gradient Boosting Trees (GBT) algorithm. The experimental results show that the application of bagging techniques can reduce misclassification and improve prediction accuracy.