Supervised machine learning models for classifying common causes of dizziness

Abstract

PurposeThe objective of this study was to use a supervised machine learning (ML) platform and a national dataset to identify factors important in classifying common types of dizziness. MethodsUsing established clinical criteria and responses to the balance and dizziness supplement from the 2016 National health Interview Survey (n = 33,028), case definitions for vestibular migraine (VM), benign paroxysmal positional vertigo (BPPV) Ménière's disease (MD), persistent postural-perceptual dizziness (PPPD), superior canal dehiscence (SCD), and bilateral vestibular hypofunction (BVH) were generated. One hundred thirty-six variables consisting of sociodemographic characteristics and medical comorbidities were used to develop decision tree models to predict these common types of dizziness. ResultsThe one-year prevalence of dizziness in the U.S. was 16.8% (5562 respondents). VM was highly prevalent, representing 4.0% of the overall respondents (n = 1327). ML decision tree models were able to correctly classify all 6 dizziness subtypes with high accuracy (sensitivity range, 70–92%; specificity range, 89–99%) using responses to questions about functional limitations due to dizziness, such as falls due to dizziness and modification of social activities due to dizziness. ConclusionsIn a large population-based dataset, supervised ML models accurately predicted dizziness subtypes according to responses to questions that do not pertain to dizziness symptoms alone.