ArticleScientific reports2022
Application of machine learning in the diagnosis of vestibular disease.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 18 citations in OpenAlex.
- Comprehensive Review of Nystagmus and Vertigo Diagnostics: From Pathological Foundations to AI-Driven Telemedicine.Sensors (Basel, Switzerland) · 2026Review
- Optimizing Dizziness Management: Impact of Audiology Triage on Care Efficiency.The Laryngoscope · 2026Article
- Interpretable Machine Learning for LPR Risk Estimation: A Single-Center Retrospective Case-Control Study.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Regression-Based Analysis of Vestibular Laboratory Tests for the Prediction of Unilateral Vestibular Schwannoma.IEEE journal of translational engineering in health and medicine · 2026Article
- Can Vascular Vertigo Be Recognized by Artificial Intelligence Methods?Noro psikiyatri arsivi · 2026Article
- A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data.Scientific reports · 2025Article
- Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.BMC infectious diseases · 2025Article
- Advanced Machine Learning for Comparative Synovial Fluid Analysis in Osteoarthritis and Rheumatoid Arthritis.Metabolites · 2025Article
- Clinical trials in otology and neurotology: state of the science.Frontiers in neurology · 2025Review
- Current insights on predicting vestibular diseases using machine learning.Turkish journal of medical sciences · 2025Review
- Machine learning-based predictive model for high- grade cytokine release syndrome in chimeric antigen receptor T-cell therapy.Frontiers in immunology · 2025Observational
- Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach.Annals of general psychiatry · 2024Article
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- "Vertigo, likely peripheral": the dizzying rise of ChatGPT.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Machine Learning-Based Dizziness/Vertigo Disease Diagnosis by Combining Medical History and Signs.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-facialeArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Machine learning is considered a potential aid to support human decision making in disease prediction. In this study, we determined the utility of various machine learning algorithms in classifying peripheral vestibular (PV) and non-PV diseases based on the results of equilibrium function tests. A total of 1009 patients who had undergone our standardized neuro-otological examinations were recruited. We applied five supervised machine learning algorithms (random forest, adaboost, gradient boosting, support vector machine, and logistic regression). After preprocessing the data, optimizing the hyperparameters using GridSearchCV, and performing a final evaluation on the test set using scikit-learn, we evaluated the predictive capability using various performance metrics, namely, accuracy, F1-score, area under the receiver operating characteristic curve, precision, recall, and Matthews correlation coefficient (MCC). All five machine learning algorithms yielded satisfactory results; the accuracy of the algorithms ranged from 76 to 79%, with the support vector machine classifier having the highest accuracy. In cases where the predictions of the five models were consistent, the accuracy of the PV diagnostic results was improved to 83%, whereas it increased to 85% for the non-PV diagnostic results. Future research should increase the number of patients and optimize the classification methods to obtain the highest diagnostic accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.