A New Knowledge-based System for Diagnosis of Breast Cancer by a combination of Affinity Propagation Clustering and Firefly Algorithm

Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in the diagnosis of breast cancer, and can help physicians for decision-making processes. This paper presents a new hybrid data mining approach to classify two groups of breast cancer patients, malignant and benign. The proposed approach, AP-AMBFA, consists of two phases. In the first phase, the Affinity Propagation (AP) clustering method is used as an instance reduction technique, which can find noisy instances and eliminate them. In the second phase, feature selection and classification are conducted by the Adaptive Modified Binary Firefly Algorithm (AMBFA) for selection of the most related predictor variables to target variables and the Support Vectors Machine (SVM) technique as classifier. It can reduce the computational complexity and speed up the data mining process. The experimental results on the Wisconsin Diagnostic Breast Cancer (WDBC) datasets show a higher predictive accuracy. The classification accuracy obtained was 98.606%, a very promising result compared to the current state-of-the-art classification techniques applied to the same database. Hence, this method will help physicians in a more accurate diagnosis of breast cancer.