Evolutionary Ensemble Model for Breast Cancer Classification

A major problem in medical science is attaining the correct diagnosis of disease in precedence of its treatment. For the ultimate diagnosis, many tests are generally involved. Too many tests could complicate the main diagnosis process so that even the medical experts might have difficulty in obtaining the end results from those tests. A well-designed computerized diagnosis system could be used to directly attain the ultimate diagnosis with the aid of artificial intelligent algorithms and hybrid system which perform roles as classifiers. In this paper, we describe a Ensemble model which uses MLP, RBF, LVQ models that could be efficiently solve the above stated problem. The use of the approach has fast learning time, smaller requirement for storage space during classification and faster classification with added possibility of incremental learning. The system was comparatively evaluated using different ensemble integration methods for breast cancer diagnosis namely weighted averaging, product, minimum and maximum integration techniques which integrate the results obtained by modules of ensemble, in this case MLP, RBF and LVQ. These models run in parallel and results obtained will be integrated to give final output. The best accuracy, sensitivity and specificity measures are achieved while using minimum integration technique.