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Cardiovascular disease (CVD) is currently the biggest single cause of mortality in the developed world, hence, the early detection of its onset is vital for effective prevention therapies. Aortic stiffness as measured by aortic pulse wave velocity (PWV) has been shown to be an independent predictor of CVD, however, the measurement of PWV is complex and time consuming. Recent studies have shown that pulse contour characteristics depend on arterial properties such as arterial stiffness. This paper presents a method for estimating PWV from the digital volume pulse (DVP), a waveform that can be rapidly and simply acquired by measuring the transmission of infra-red light through the finger pulp. PWV and DVP were measured on 461 subjects attending a clinic in South East London. Techniques for extracting features from the DVP contour based on physiology and information theory were compared. Low and high stiffness were defined according to a threshold level of PWV chosen to be 10 m/s. Using a support vector machine-based classifier, it is possible to achieve high overall classification rates on unseen data. Further, the use of support vector regression techniques lead to a direct real-valued estimate of PWV which outperforms previous methods based on multilinear regression. We, therefore, conclude that support vector machine-based classification and regression techniques provide effective prediction of arterial stiffness from the simple measurement of the digital volume pulse. This technique could be usefully employed as a cheap and effective CVD screening technique for use in general practice clinics.