Support Vector Machines (SVMs) technique is a recent method for empirical data modelling applied to pattern recognition problems. The aim of the present study is to test SVMs performance when applied to a specific medical classification problem - diagnosis of iron-deficiency anemia in uremic patients - and to compare the results with those obtained by traditional techniques such as logistic regression and discriminant analysis. Models have been compared both in learning and validation phases. All methods performed well (accuracy > 80%). Sensibility of SVMs is always higher than the ones of the other models; specificity and accuracy are lower in one repetition over three. Within the limits of the present study, we can say that the SVMs can constitute an innovative method to approach clinical classification problem on which to further invest.
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