  In this paper, we review data mining approaches for health applications. Our
focus is on hardware-centric approaches. Modern computers consist of multiple
processors, each equipped with multiple cores, each with a set of
arithmetic/logical units. Thus, a modern computer may be composed of several
thousand units capable of doing arithmetic operations like addition and
multiplication. Graphic processors, in addition may offer some thousand such
units. In both cases, single instruction multiple data and multiple instruction
multiple data parallelism must be exploited. We review the principles of
algorithms which exploit this parallelism and focus also on the memory issues
when multiple processing units access main memory through caches. This is
important for many applications of health, such as ECG, EEG, CT, SPECT, fMRI,
DTI, ultrasound, microscopy, dermascopy, etc.
