Machine Learning in Diagnosis Support with Posturography Data

from Fujimoto et al. (Otol. Neurotol., 2014), including posturography and vestibular function data and found that machine learning algorithms can be successfully used to predict vestibular dysfunction as iden-tified using caloric testing with the dataset of the center of pressure sway during posturography. Some of the points to be considered for practical application of machine learning in the field of vertigo research include the following: clinical data contain many errors, and database errors may occur frequently, the accuracy of clinical examinations should be taken into ac-count, the difference between the acute and chronic phases of disease should be taken into ac-count, and the dizziness symptom varies among cases. In order to achieve better accuracy, a large amount of data is required, and multi-institutional joint research should be considered.