Machine Learning Models for Predicting Vestibular Function After Cochlear Implantation
Mengya Shen1
Xiaozhang Zhu2
Weirui Zhang2
Shujin Xue3
Xingmei Wei3
Ying Kong3
Jiaqiang Sun4
Yongxin Li3
Haihui Wang2
1.Department of Otorhinolaryngology Head and Neck Surgery,The First Affiliated Hospital of USTC,Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei,Anhui 230001,China;Key Laboratory of Otolaryngology Head and Neck Surgery,Department of Otorhinolaryngology Head and Neck Surgery,Beijing Tongren Hospital,Capital Medical University,Beijing 100730,China2.Laboratory of Haihui Data Analysis,School of Mathematical Sciences,Beihang University,Beijing 102206,China3.Key Laboratory of Otolaryngology Head and Neck Surgery,Department of Otorhinolaryngology Head and Neck Surgery,Beijing Tongren Hospital,Capital Medical University,Beijing 100730,China4.Department of Otorhinolaryngology Head and Neck Surgery,The First Affiliated Hospital of USTC,Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei,Anhui 230001,China
摘要：Objective:To assess the effectiveness of machine learning in automating the prediction of vestibular abnormalities after cochlear implantation(CI)in patients with sensorineural hearing loss(SNHL),with the goal of developing a practical model that can accurately predict long-term vestibular function outcomes and identify associated risk factors.Methods:Clinical data,including imaging,vestibular evoked myogenic potentials(VEMPs),and auditory information,were collected from patients with sensorineural hearing loss(SNHL)before and after CI.The decision tree algorithm was employed to address missing values and screen pre-CI clinical features.Six machine learning methods were subsequently utilized to predict the relationships between the extracted features and post-CI vestibular dysfunction.The best-performing method determined the ranking of feature importance,which was regarded as risk factors for predicting symptoms and VEMPs results after CI.Results:Logistic regression models effectively predicted both post-CI vestibular dysfunction and abnormal cervical VEMP(cVEMP),with accuracies of 80%and 78%,respectively.The relative importance of the features,in descending order,was as follows:cVEMP latency,cVEMP amplitude,and residual hearing threshold.Moreover,the support vector machine(SVM)model attained an accuracy of 88%in predicting abnormal ocular VEMP(oVEMP)post-CI.For the SVM model,the feature importance ranking was as follows:oVEMP latency,oVEMP amplitude,and residual hearing threshold.Conclusions: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.
机标关键词：learningfunctionmodelsaftercochlearimplantationmachinepredicting
论文发表日期：2025-10-30
在线出版日期：2025-11-27（本平台首次上网日期，不代表文献的发表时间）
页数：11( 225-235 )
英文信息