@Article{info:doi/10.2196/34126,
author="Yu, Fangzhou
and Wu, Peixia
and Deng, Haowen
and Wu, Jingfang
and Sun, Shan
and Yu, Huiqian
and Yang, Jianming
and Luo, Xianyang
and He, Jing
and Ma, Xiulan
and Wen, Junxiong
and Qiu, Danhong
and Nie, Guohui
and Liu, Rizhao
and Hu, Guohua
and Chen, Tao
and Zhang, Cheng
and Li, Huawei",
title="A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study",
journal="J Med Internet Res",
year="2022",
month="Aug",
day="3",
volume="24",
number="8",
pages="e34126",
keywords="vestibular disorders; machine learning; diagnostic model; vertigo; ENT; questionnaire",
abstract="Background: Questionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making. A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular disorders. Objective: This study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo. Methods: In this multicenter prospective study, patients presenting 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. Patients who received only 1 final diagnosis by their treating specialists for their primary complaint were included in model development and validation. The data of patients enrolled before February 1, 2021 were used for modeling and cross-validation, while patients enrolled afterward entered external validation. Results: A total of 1693 patients were enrolled, with a response rate of 96.2{\%} (1693/1760). The median age was 51 (IQR 38-61) years, with 991 (58.5{\%}) females; 1041 (61.5{\%}) patients received the final diagnosis during the study period. Among them, 928 (54.8{\%}) patients were included in model development and validation, and 113 (6.7{\%}) patients who enrolled later were used as a test set for external validation. They were 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{\%} CI 0.917-0.962) in cross-validation and 0.954 (95{\%} CI 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 studies with a larger sample size and the participation of neurologists will help assess the generalization and robustness of this machine learning method. ",
issn="1438-8871",
doi="10.2196/34126",
url="https://www.jmir.org/2022/8/e34126",
url="https://doi.org/10.2196/34126",
url="http://www.ncbi.nlm.nih.gov/pubmed/35921135"
}