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Breast Cancer Survivability Predictor Using Adaboost and CART Algorithm

R.K.Kavitha, Dr.D.DoraiRangasamy

Breast cancer is the second leading cancer for women in developed countries including India. Many new cancer detection and treatment approaches were developed, the cancer incidences and death of breast cancer decreased constantly. The patients are concerned about survival time after diagnosis in order to plan regarding their treatments. It is difficult for a physician to have accurate answers about prognosis. Data mining techniques are used to obtain useful information from the large amounts of data which can help the physician for decision making regarding the prognosis. This paper studies the performance comparison of Adaboost algorithm which classifies data as linear combination and CART (Classification and regression trees) which classifies data by constructing decision tree in predicting the survivability of breast cancer patients.