A New Feature Selection Method for Oral Cancer Using Data Mining Techniques

Abstract: The term cancer is used generically for more than 100 different diseases including malignant tumours of different sites (such as breast, cervix, prostate, stomach, colon/rectum, lung, mouth, leukaemia, sarcoma of bone, Hodgkin disease, and non-Hodgkin lymphoma). Common to all forms of the disease is the failure of the mechanisms that regulate normal cell growth, proliferation and cell death. Ultimately, there is progression of the resulting tumour from mild to severe abnormality, with invasion of neighbouring tissues and, eventually, spread to other areas of the body. The primary risk factor for developing oral cancer is tobacco use. Smoking cigarettes, cigars, and pipes all increase risk of oral cancer. Smokeless tobacco, often called "dip" or "chew," also heighten the risk. Alcohol consumption is another habit that is strongly associated with the development of oral cancer. This research uses data mining technology such as classification, clustering and prediction to identify potential oral cancer patients.Apriori algorithm is the originality algorithm of Boolean association rules of mining frequent item sets. The datamining methods and techniques will be explored to identify the suitable methods and techniques for efficient classificationof data.The data mining techniques are effectively used to extract meaningful relationships fromthese data. Genetic algorithms were applied to association and classification techniques.

Keywords: Cancer; Oral Cancer; Data Mining; Classification; Decision tree; Apriori algorithm; Association rule; Genetic algorithm.