Prognosis of breast cancer based on a fuzzy classification method

Learning and classification techniques have shown their usefulness in the analysis of ana-cyto-pathological cancerous tissue data to develop a tool for the diagnosis or prognosis of cancer. The use of these methods to process datasets containing different types of data has become recently one of the challenges of many researchers. This paper presents the fuzzy classification method LAMDA with recent developments that allow handling this problem efficiently by processing simultaneously the quantitative, qualitative and interval data without any preamble change of the data nature as it must be generally done to use other classification methods. This method is applied to perform breast cancer prognosis on two real-world datasets and was compared with results previously published to prove the efficiency of the proposed method.