Özet:

Breast cancer is one among the deadly diseases in women that have the high death percentage in the world. An accurate and initial recognition of breast cancer via dataset is still a difficult task.  Due to the huge dispersion of the disease, automatic recognition schemes can benefit physicians to categorize the tumors as benign or malignant. Though, carrying out an automatic recognition is time intense and produces reduced accuracy.  Meanwhile numerous statistics of features are existing in the dataset. Over the centuries, meta-heuristic optimization methods have remained and useful for Feature Selection (FS), as these are able to overwhelmed the limits of old optimization methods. Data mining techniques can sustenance doctors in analysis decision-making method. This paper reports feature selection and classification method for breast cancer analysis. The proposed system has dual steps. In the first step, in order to remove trivial features, wrapper method using Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO)built on FS for variety of useful and important features. In AMEHO algorithm, clan bring up-to-date operator for sorting according to appropriateness and three degrees of freedom (. Local optima problematic is resolved by announcing adaptive mutation operator. This method decreases the computational difficulty and rapidity of data mining method. This FS algorithm is used to improve the accuracy of analysis (benign and malignant). In the following stage, Kernel Extreme Learning Machine (KELM) classifier is active to choose for two diverse categories of subjects with or without breast cancer. To assess efficiency of proposed process, three different classifiers such as K Nearest Neighbour (KNN), Naïve Bayes (NB), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) on Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Original Breast Cancer (WOBC) using University of California, Irvine (UCI) repository. Performance metrics such as Precision, Recall, F-measure, Accuracy, Area Under Curve (AUC), statistical measure via k-fold cross proof is taken for linking the proposed system with the present works.

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