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Using the SAS ® System and SAS ® Enterprise Miner TM for Data Mining : A study of Cancer Survival at Mayo Clinic

This paper evaluates and predicts a certain epidemiological (cancer survival) condition using data-mining techniques in SAS®. A data set that contains information about the survival of lung-cancer patients from a study at the Mayo Clinic was extracted from the R survival package. Data-mining techniques—namely linear and logistic regression models, regression and classification trees, and nearest-neighbor analysis—are used for the analysis to see which method is best for determining cancer survival. Both a continuous response variable and a dichotomous response variable are selected and used to evaluate cancer survival of the patients. Linear regression, regression trees, and nearest-neighbor analysis are used to analyze the continuous response variable; logistic regression, classification trees, and nearest-neighbor analysis are used for the dichotomous response variable. The results show that the dichotomous response variable using the nearest-neighbor analysis (using 8 neighbors) and the classification trees are best for the analysis of cancer survival in this particular data set, with a correct classification rate of 78.6% for both methods.