Machine Learning-Based Dizziness/Vertigo Disease Diagnosis by Combining Medical History and Signs

ImportanceClinicians face great challenges in diagnosing dizziness/vertigo disease due to its subjectivity. Currently, there is an absence of machine learning model that could make full use of the information gained from both medical history and physical signs.ObjectiveTo develop and validate a machine learning model based on medical history and physical signs for dizziness/vertigo disease diagnosis, relieving the burden of diagnosis for clinicians.DesignA retrospective cohort study.SettingTertiary referral center.ParticipantsThis study included 1003 patients conformed to the inclusion criteria at the neuro-otologists' clinics.ExposuresThirty-one medical history items, and 9 bedside examination signs recorded by routinely performing a detailed ocular motor examination using video goggles.Main Outcome MeasuresThe accuracy, precision, recall, F1 scores, and Matthews' correlation coefficient of disease diagnosis.ResultsOn the collected dataset of 16 categories of dizziness/vertigo diseases, the proposed model achieved an accuracy of 98.11% and an F1 score of 95.43%. The model demonstrated its optimal robustness when tested with datasets containing added noise. Additionally, an analysis of the correlation between medical history and signs was conducted, along with several case studies.ConclusionsA machine learning-based model was proposed for the diagnosis of dizziness/vertigo, which effectively combined patients' medical history and signs. In terms of diagnostic accuracy, it outperforms models that rely solely on either medical history or signs for diagnosis.RelevanceThe proposed method can effectively combine the patient's medical history and physical sign information to make the diagnosis of dizziness/vertigo disease, which has the potential to relieve the burden of diagnosis for clinicians to a certain extent.