An Evaluation study of Oral Cancer Detection using Data Mining Classification Techniques

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S. Prasanna
K.Govinda, U.Senthil Kumaran


Cancer detection is one of the important research topics in medical science. Oral cancer is the sixth most common cancer in the world. It is one of the most prevalent cancers in the developing countries of South Asia accounting for one third of the world burden. In India oral cancer is the most common cancer that occurs. Sixty percent of the cancers are advanced by the time they are detected. In this paper we have implemented two techniques such as Naïve Bayesian and Support Vector Machine (SVM) and compared the results to show which technique is the best. Current predictive model design in medical oncology literature is dominated by linear and logistic regression techniques. In IPPSCD (Intelligent Prognosis Prediction System for Cancer Disease) a database of cancer patient performance is constructed. Data mining techniques will be used to analyze the database to predict disease based on causative factors. Both classification and regression algorithms are to be considered

Keywords: SVM, LIF, CCD , HPLC, MPM, SHG.


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