Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method

Abstract

Worldwide, breast cancer is one of the most threatening killers to mid-aged women. The diagnosis of breast cancer aims to classify spotted breast tumor to be Benign or Malignant. With recent developments in data mining technique, new model structures and algorithms are helping medical workers greatly in improving classification accuracy. In this study, a model is proposed combining ensemble method and imbalanced learning technique for the classification of breast cancer data. First, Synthetic Minority Over-Sampling Technique (SMOTE), an imbalanced learning algorithm is applied to selected datasets and second, multiple baseline classifiers are tuned by Bayesian Optimization. Finally, a stacking ensemble method combines the optimized classifiers for final decision. Comparative analysis shows the proposed model can achieve better performance and adaptivity than conventional methods, in terms of classification accuracy, specificity and AuROC on two mostly-used breast cancer datasets, validating the clinical value of this model.