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    A hybrid classification algorithm approach for breast cancer diagnosis
    (Ieee, 2016) Abed, Baraa M.; Shaker, Khalid; Jalab, Hamid A.; Shaker, Hothefa; Mansoor, Ali Mohammed; Alwan, Ahmad F.; Al-Gburi, Ihsan Salman
    Early diagnosis of Breast Cancer is significantly important to treat the disease easily therefore it is necessary to develop techniques that can help physicians to get accurate diagnosis. This study suggests a hybrid classification algorithm which is based upon Genetic Algorithm (GA) and k Nearest neighbor algorithm (kNN). GA algorithm has been used for its primary purpose as an optimization technique for kNN by selecting best features as well as optimization of the k value, while the kNN is used for classification purpose. The planned algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases using different datasets in which the first is Wisconsin Breast Cancer Database (WBCD) and the second one is Wisconsin Diagnosis Breast Cancer (WDBC) which has changes in the number of attributes and number of instances. The proposed algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved 99% accuracy.