A New Method for Diagnosing Breast Cancer using Firefly Algorithm and Fuzzy Rule based Classification

Predicting and diagnosing various diseases are two effective components in medical science. Recently, with increasing expansion of science, using the decision support system can help significantly to the therapeutic policies of physician. For this purpose, by using artificial intelligent systems in this study, we are trying to predict and diagnose the breast cancer which is one of the most prevalent cancers among the women. In this study, three datasets of Wisconsin cancer diagnosis were used which were obtained by FNA test. Process of breast cancer diagnosis was investigated by a new approach. With the help of fuzzy rule based classification systems, datasets were classified. Rule weight learning algorithm has been used to improve the classification system which minimizing the classification error on the training data using the rule weight adjustment. An integrated method was also presented to reduce the dimensions of datasets features using the firefly algorithm and statistical repair mechanism. Comparing the performance of the aforementioned method with other ones shows the desired performance and high accuracy of the proposed method.