Decision Support System for Improving Breast Cancer Diagnosis Using Ensemble Learning

Abstract

Breast cancer (BC) is one of the leading causes of death in women worldwide. Early diagnosis of this disease can save many women’s lives. The Breast Imaging reporting and Data System (BIRADS) is a standard method developed by the American College of Radiology (ACR). However, physicians have had a lot of contradictions in determining the value of BIRADS, and all aspects of patients have not been considered in diagnosing this disease using the methods that have been used so far. In this article, a novel decision support system (DSS) has been presented. In the proposed DSS, firstly, c-mean clustering was used to determine the molecular subtype for patients who did not have this value by combining the mammography reports processing along with hospital information systems (HIS) obtained from their electronic files. Then several classifiers such as convolutional neural networks (CNN), decision tree (DT), multi-level fuzzy min-max neural network (MLF), multi-class support vector machine (SVM), and XGboost were trained to determine the BIRADS. Finally, the values obtained by these classifiers were combined using ensemble learning with the majority voting algorithm to obtain the appropriate value of BIRADS. This helps physicians in the early diagnosis of BC. Finally, the results were evaluated in terms of accuracy, specificity, sensitivity, positive predicted value (PPV), negative predicted value (NPV), f1-measure and balanced accuracy by the confusion matrix. The obtained values were, 87.77 %, 61.81 %, 92.74 %, 56.82 %, 92.75 %,  69.94 %, and 77.28 %, respectively.