Hybrid intelligent system-based rough set and ensemble classifier for breast cancer diagnosis

The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Breast cancer is becoming a leading cause of death among women in the whole world; meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This paper presents a hybrid intelligent system for recognition of breast cancer tumors. The proposed system includes two main modules: the feature extraction module and the predictor module. In the feature extraction module, rough set theory is used to preprocess the attributes on condition that the important information is not lost, deletes redundant attributes and conflicting objects from decision table. In the predictor module, a combined classifier is proposed based on K-nearest neighbor classifier. Experiments have been conducted on a widely used Wisconsin breast cancer dataset taken from University of California Irvine. Experimental results show that the proposed hybrid system can improve the rate of correct diagnosis of cases. The proposed combined classifier with rough set-based feature selection achieves 99.41 % classification accuracy and uses only 4 features which is the best shown to date. Different performance metrics are used to show the effectiveness of the proposed hybrid system. With these results, the proposed method is very promising compared to the previously reported results and can be used confidently for other breast cancer diagnosis problems.