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This paper explores the use of different support vector machines (SVM) kernels, and combinations of kernels, to ascertain the diagnostic accuracy of a screen film mammogram data set containing ≅ 2500 samples from five different institutions. This research has demonstrated that: (1) specificity improves, on the average, of about 4% at 100% sensitivity and about 18%, on the average, at 98% sensitivity. This means that approximately 52 and 134 women would not have to undergo biopsy, at 100% and 98% sensitivity, when compared to the case of every women being biopsied, which would be necessary to identify all cancers in the absence of a computer aided diagnostic (CAD) process, (2) positive predictive value (PPV) at these same values of sensitivity are much better, ranging from 48% to 51 % as sensitivity is decreased from 100 to 98%. Finally, the average specificity over the top 10% or the ROC curve (which is the average specificity between 90-100% sensitivity) is about 30%. This means that, on the average, 440 women would not have to undergo biopsy, when compared to the case of all women being biopsied.