A self-constructing neuro-fuzzy classifier for breast cancer diagnosis using swarm intelligence

Abstract

In this paper, a self-constructing neuro-fuzzy (SCNF) classifier optimized by swarm intelligence technique is proposed for breast cancer diagnosis. The first step in the design is the definition of the fuzzy network structure. Accordingly, a rule generation approach with self-constructing property is proposed. Based on similarity measures, the given input-output patterns are organized into clusters. Then, membership functions are generated roughly to form a fuzzy rule base. To achieve accurate learning, particle swarm optimization (PSO) algorithm is used to adjust consequent and antecedent parameters of the obtained rules. Accordingly, a weighted function is constructed to design the objective function of the PSO, which takes into account the specificity, the sensitivity and the total classification accuracy of the proposed SCNF classifier. The proposed SCNF classifier is evaluated on the widely used Wisconsin breast cancer dataset (WBCD) for breast cancer diagnosis. Experimental results confirm that the proposed model is able to detect breast cancer with a classification accuracy of more than 99%. A comparative study has been elaborated confirming the best performance of the proposed classifier.