Breast Cancer Diagnosing System: Using a Rough Set-Ensemble Classifier Approach

Abstract Breast cancer occurs when normal breast cells turn cancerous, grow abnormally and form tumors. The most common cancer impacting women worldwide is breast cancer. Diagnosing breast cancer early and accurately is crucial for giving the correct treatment and ensuring patients receive the best care possible. Due to human error, misdiagnosis is a possibility in the medical field. Over-diagnosis can cause patients to go through unnecessary treatments. Under-diagnosis can allow malignant tumors to become more aggressive and life-threatening. The aim of our research is to create a dependable model to correctly diagnose breast cancer. We propose to use a rough set ensemble classifier approach to assist doctors in making more accurate diagnosis. The rough set reduct algorithm will be used for feature reductions and the model will be built with logistic regression algorithm, Support Vector Machine (SVM) algorithms and random forest algorithm. The proposed model produced an accuracy of 93% for logistic regression algorithm, 97% for SVM, and 92% for Random Forest when classifying the image data and overall produced a 96% accuracy.