Oral cancer detection using data mining tool

Previously cancer was an incurable disease, but now with the advancement in technology it has been successful in becoming a curable disease. Oral cancer is the unstoppable increase in the number of cells or mutation that is formed and has the capability to affect the neighboring tissues. In this paper different algorithms of data mining will be used to detect oral cancer. Data mining is referred to a prominent technique employed by various health institutions for classification of life threatening diseases, e.g. cancer, dengue and tuberculosis. In our proposed approach WEKA is applied with ten cross validation to calculate and collate output. WEKA consists of a large variety of data mining machine learning algorithms. First we have classified the oral cancer dataset and then analyzed various data mining methods in WEKA through Explorer and Experiment interfaces. The prime aim is to classify the dataset and help to collect useful material from the data and comfortably choose an appropriate algorithm for accurate prognostic model from it.