Data mining techniques for vestibular data classification

The vestibular system controls motor functions to providing the sense of balance and spatial orientation. The damages of this system are generally estimated with the vestibular evoked myogenic potentials (VEMPs) test. In the proposed work the data of this test are analyzed using a specific algorithm and developing a dedicate framework to support clinical activity, with an easy-to-use GUI. The data have been collected and used for extracting a prediction of a pathology distribution by using data mining techniques. In to the studies two different algorithms. In a first step, the Bayesian classifier has been chosen to extract the predisposition of female to lodge diseases of the auditory system compared to the male. In the final contribution, the nearest neighbour classification algorithm has been choosing for as classifier. Results show a better classification of the pathologies as a function of sex respect to the results obtained with the previous classifier.