Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 12, 2021
Date Accepted: Jun 13, 2022
A questionnaire-based ensemble learning model to predict the diagnosis of vertigo: model development and validation study
ABSTRACT
Background:
Questionnaires have been used to predict the diagnosis of vertigo and assist clinical decision making. A questionnaire-based machine learning model is expected to improve the diagnostic efficiency of vestibular disorders.
Objective:
To develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo.
Methods:
In this multicenter prospective study, patients presented with vertigo entered a consecutive cohort at their first visit to the ENT and vertigo clinics of 7 tertiary referral centers from August 2019 to March 2021, with a follow-up period of 2 months. All participants completed a diagnostic questionnaire after eligibility screening. Participants who received only one final diagnosis by their treating specialists for their primary complaint were included in model development and validation. Data of patients enrolled before Feburary 1, 2021 were used for modeling and cross validation, while patients enrolled afterwards entered external validation.
Results:
A total of 1693 patients were enrolled, with a response rate of 96%. The median age was 51 years (interquartile range, 38 to 61) with 991 (58.5%) female. 1041 (61.5%) patients were included in model development and validation, and classified into 5 diagnostic categories. We compared 9 candidate machine learning methods, and the recalibrated model of light gradient boosting machine achieved the best performance, with an area under the curve of 0.937 (95% confidence interval, 0.917-0.962) in cross validation and 0.954 (95% confidence interval, 0.944-0.967) in external validation.
Conclusions:
The questionnaire-based light gradient boosting machine was able to predict common vestibular disorders and assist decision making in ENT and vertigo clinics. Further study with lager sample size and the participation of neurology specialists will help assess its generalization and robustness. Clinical Trial: Name: Design and application of Otogenic Vertigo AI Recognition (OVerAIR) registration number: No. ChiCTR2000032904 URL: https://www.chictr.org.cn/showproj.aspx?proj=53595
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