Feature Selection in Proton Magnetic Resonance Spectroscopy for Brain Tumor Classification

H-MRS is a technique that uses response of protons under certain magnetic conditions to reveal the biochemical structure of human tissue. An important application is found in brain tumor diagnosis, due to the known complications of physical exploration and as a help to other kind of non invasive methods. It is possible to analize spectral data with machine learning methods to classify tumor classes in an automated fashion. One important characteristic of these data is their high dimensionality. In this work we present a contribution to lighten this situation with an algorithm based on entropic measures of subsets of spectral data. Experimental results show that the approach used has a good classification performance, both in terms of prediction accuracy and number of involved spectral frequencies.