Supervised machine learning models for classifying common causes of dizziness.

PURPOSE: The 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. METHODS: Using 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. RESULTS: The 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. CONCLUSIONS: In 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.