Coronavirus Optimization Algorithm for Breast Cancer Classification
Obaid S. Nassif, Khalid Shaker · 2021
Early diagnosis of diseases is necessary and important to assist doctors in order to treat and control diseases such as breast cancer early. A high accuracy machine learning techniques should be available to assist clinician to come out with accurate diagnosis. In this work we tackle this problem using the coronavirus algorithm. The Coronavirus algorithm was used as a feature optimization method, while J48 and PART were used for classification. The proposed approach has been tested on the Wisconsin Diagnosis Breast Cancer (WDBC) dataset located in the UCI Machine Learning Database repository. Experimental results show that our approach is able to generate competitive results when compared with previous available approaches. The proposed approach could obtain an accuracy of 94.01 % for the j48 algorithm and an accuracy of 94.18% for the PART algorithm.