Machine Learning Models for Predicting Vestibular Function After Cochlear Implantation
This study successfully leverages machine learning techniques, specifically support vector machines (SVM) and logistic regression models, to predict the impact of CI on vestibular function. These predictive models provide valuable insights for presurgical planning and decision-making in CI procedures. Moreover, the findings highlight the critical risk factors associated with vestibular dysfunction, offering a robust reference for guiding vestibular rehabilitation strategies.
Authors
- Weirui Zhang (ORCID: https://orcid.org/0000-0002-4048-392X)
- Ying Kong (ORCID: https://orcid.org/0000-0001-8734-7450)
- Xiaozhang Zhu (ORCID: https://orcid.org/0000-0002-7411-660X)
- Shujin Xue (ORCID: https://orcid.org/0000-0002-9805-1759)
- Jiaqiang Sun (ORCID: https://orcid.org/0000-0002-1673-7782)
- Haihui Wang (ORCID: https://orcid.org/0000-0001-6345-0349)
- Xingmei Wei (ORCID: https://orcid.org/0000-0001-7188-485X)
- Mengya Shen (ORCID: https://orcid.org/0000-0002-6086-7736)
- Yongxin Li (ORCID: https://orcid.org/0000-0001-6267-5730)
Institutions
- University of Science and Technology of China (CN)
- Beijing Tongren Hospital (CN)
- Beihang University (CN)
Publication Details
- Journal
- Journal of Otology
- Published
- 2025-10-01
- DOI
- https://doi.org/10.26599/joto.2025.9540035
- Primary Topic
- Vestibular and auditory disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China