Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability.

ABSTRACT: Objective: To evaluate various machine learning algorithms in predicting peripheral vestibular dysfunction using the dataset of the center of pressure (COP) sway during foam posturography measured from patients with dizziness. Study Design: Retrospective study. Setting: Tertiary referral center. Patients: Seventy-five patients with vestibular dysfunction and 163 healthy controls were retrospectively recruited. The dataset included the velocity, the envelopment area, the power spectrum of the COP for three frequency ranges and the presence of peripheral vestibular dysfunction evaluated by caloric testing in 75 patients with vestibular dysfunction and 163 healthy controls. Main Outcome Measures: Various forms of machine learning algorithms including the Gradient Boosting Decision Tree, Baggi

SUBMITTER: Kamogashira T 

PROVIDER: S-EPMC7013037 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature