Breast Cancer Classification Using Ensemble of Machine Learning Boosting Algorithms

Abstract

Breast Cancer (BC) is one of the numerous typical diseases worldwide, occurring in 22.9% of all cancers in women and causing 13.7% of cancer deaths. The BC prognosis is highly demanded to increase the survival rate of the patient suffering from BC. Throughout this paper, an automated decision-making pipeline for BC detection has been proposed, incorporating Machine Learning (ML) algorithms like Gaussian Naive Bayes (GNB), Random Forest (RF), XGBoost (XGB), AdaBoost (AdB), and preprocessing such as Outlier Rejection (OR) and Attribute Selection (AS). A weighted ensemble of ML models has been recommended in the introduced pipeline. The experiments are trained and evaluated using Breast Cancer Wisconsin’s public dataset from UCI Repository, employing five-fold cross-validation. The best possible accuracy obtained from the proposed framework is 97.0%, with the utilization of seventeen features out of a total of thirty. The observed results conclude that a weighted ensemble of AdB and XGB in conjunction with OR and AS as a preprocessing can successfully enhance the BC detection outcomes with a significantly short execution time of 2.10 seconds.