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The significant potential for early and accurate detection of Alzheimer's disease (AD) through neuroimaging data is becoming increasingly attractive in view of the possible advent of drugs which are able to modify or delay disease progression. In this paper, we aimed at developing an effective machine learning scheme which leverages structural magnetic resonance imaging features in order to identify and discriminate individuals affected by mild AD on a single subject basis. Selected features included one- and two-way combinations of subcortical and cortical volumes as well as cortical thickness and curvature of numerous brain regions which are known to be vulnerable to AD. Additionally, several feature combinations were fed into support vector machines (SVMs) as well as Naïve Bayes classifiers in order to compare scheme accuracy. The most efficient combination of features and classification scheme, which employed both subcortical and cortical volumes feature vectors and a SVM classifier, was able to distinguish mild AD patients from healthy controls with 86% accuracy (82% sensitivity and 90% specificity). While this investigation is of preliminary nature, and further efforts are currently underway towards automated feature selection, best classifier determination and parameter optimization, our results appear very promising in terms of automated high-accuracy discrimination of disease stages which cannot easily be distinguished though routine clinical investigation.