'Better Ear' Self-Perception Aligns with Audiometry: Insights from Machine-Learning Modeling

Accurate self-perception of interaural hearing asymmetry is crucial for clinical decision-making and communication strategies, yet the relationship between objective audiometric patterns and subjective awareness remains poorly characterized. Using nationally representative data from the U.S. National Health and Nutrition Examination Survey, this study employed a machine learning approach to model the probability that an individual's self-reported "better-hearing" ear matches the audiometrically defined one.

A Light Gradient Boosting Machine classifier was trained exclusively on objective measures-ear-specific pure-tone averages (PTA) and interaural asymmetry metrics-to predict correct subjective identification.

The model demonstrated robust performance on a held-out test set, with an accuracy of 0.85, precision of 0.84, recall of 0.98, an F1-score of 0.91, and an area under the receiver operating characteristic curve of 0.83. Explainable artificial intelligence analysis revealed that the absolute magnitude of interaural PTA asymmetry was the dominant predictor of correct self-report, while the signed direction of asymmetry contributed minimally.

The results indicate that subjective awareness is strongly tied to the size of the hearing difference between ears rather than its direction and becomes more reliable with greater asymmetry. These findings indicate that a simple "better ear" self-report item captures meaningful audiometric information, supporting its potential use in clinical triage and public health surveillance, while also highlighting the need for caution in cases of mild asymmetry where misclassification is more likely.