Detection of Breast Cancer Progress Using Adaptive Nero Fuzzy Inference System and Data Mining Techniques

Subject Areas : Journal of Computer & Robotics

Hengameh  Mahdavi ¹ (Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)

Received: 2012-06-10 Accepted : 2012-09-05 Published : 2013-02-01

Keywords: Clustering, Classification, ANFIS,

Abstract :

Prediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. Nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the previous information of patients records. In this paper has been used adaptive nero fuzzy inference system and data mining techniques for processing of input data and the educational combined algorithm for arranging of parameters of input functions. It has used also the downward gradient algorithm for arranging of unlined input parameters and the algorithm of the least of squares for arranging of lined output parameters. It has been used the data the institute of oncology Ljubljana of Yugoslavia that contain the information of 1090 the breast cancer patients. The results show the suggesting system has 89% accuracy in the diagnosis of progressing the breast cancer, which has improved by compared with neural network classification method.