Indian Journal of Science and Technology

Abstract

Background/Objectives: Detection and analysis of critical diseases such as breast cancer is a significant domain of data mining analysis and research. In this research study, we propose a neuro-fuzzy classification method for breast cancer detection. Methods/Statistical Analysis: The proposed neuro-fuzzy method considers the pattern-wise degree of memberships of breast cancer databases to the existing data classes that are accomplished using a fuzzification method. The method produces a membership matrix with an element count identical to the product of the number of data records and data classes present. These matrix elements are then used as input to a neural network. Findings: We apply our method to three UCI databases namely WBC, WDBC and MM. The research work aims to recognize breast cancer disease using the proposed method and then compare its performance with two well-known classification algorithms namely Multilayer Perceptron and Support Vector Machine. We use here 10-fold cross validation technique for performing simulation. Different measures, for instance, root-mean-square error, kappa statistic, accuracy, false-positive rate, truepositive rate, precision, recall and f-measure are used to perform numerical analysis of the simulated results. All these evaluation measures support the supremacy of our proposed method. Application/Improvements: The suggested method has great potential in terms of classification capability and predictive power to use in the fields of Medical Science and Bioinformatics. 

Keywords: Breast Cancer, Classification, Data Mining, Multilayer Perceptron, Neuro-Fuzzy, Support Vector Machine