A fuzzy classification system based on memetic algorithm for cancer disease diagnosis

Classification systems have been widely utilized in medical domain to explore patient's data and extract a predictive model. This model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. Cancer disease is one of the main research topics in the medical field. We are looking for to develop a computer system for powerful and reliable cancer diagnostic model development based on microarray data. An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. The aim of this paper is to use a Memetic (Genetic Local Search) classification system to extract a set of fuzzy rules for diagnosis of cancer disease. A new memetic algorithm has been proposed which is capable of extracting interprétable and accurate fuzzy if-then rules from cancer data. In this paper, we applied our system on 14_Tumors dataset and we'll show that our approach is useful in cancer tumor detection based on the results.