Abstract
In urologic practice there exists a need to make clinical predictions for individual patients. Predictions may involve the stratification of patients into risk groups, diagnosis, prediction of cancer stage, prediction of treatment outcomes, or likelihood of disease recurrence. Traditionally, statistical classification models have addressed these predictions. These models assume, at best, fixed statistical relationships that allow only limited types of relatively simple, nonlinear, intervariable interactions and, at worst, assume linear relationships among all variables. Because medical data are inherently “noisy,” have wide variability, are not usually normally distributed, and often exhibit significant nonlinear intervariable relationships, statistical models often fall short of the desired accuracy when used in clinical urologic practice.1
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