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

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

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.Patien...

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Bibliographic Details
Main Authors: Teru Kamogashira, Chisato Fujimoto, Makoto Kinoshita, Yayoi Kikkawa, Tatsuya Yamasoba, Shinichi Iwasaki
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-02-01
Series:Frontiers in Neurology
Subjects:
posturography tests
machine learning (artificial intelligence)
vestibular dysfunction
Gradient Boosting Decision Tree (GBDT)
hyperparameter
Online Access:https://www.frontiersin.org/article/10.3389/fneur.2020.00007/full

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https://www.frontiersin.org/article/10.3389/fneur.2020.00007/full

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