Predicting Depression from Hearing Loss Using Machine Learning
Objectives: Hearing loss is the most common sensory loss in humans and carries an enhanced risk of depression. No prior studies have attempted a contemporary machine learning approach to predict depression using subjective and objective hearing loss predictors. Our objective was to deploy supervised machine learning to predict scores on a validated depression scale using subjective and objective audiometric variables and other health determinant predictors. Methods: We used a large predictor
... large predictor set of health determinants from the National Health and Nutrition Examination Survey (NHANES) 2015-2016 database to forecast scores on a validated instrument to screen for the presence and severity of depression (PHQ-9). After model training, the relative influence of individual predictors on depression scores was stratified and analyzed. Model prediction performance was determined by prediction error metrics. Results: The test-set mean absolute error was 3.03 (CI 95%: 2.91-3.14) and 2.55 (CI 95%: 2.48-2.62) on datasets with audiology-only predictors and all predictors, respectively, on the PHQ-9 27-point scale. Participants self-reported frustration when talking to members of family or friends due to hearing loss was the fifth-most influential of all predictors. Of the top ten most influential audiometric predictors, five were related to social contexts, two for significant noise exposure, two objective audiometric parameters, and one presence of bothersome tinnitus. Conclusions: Machine learning algorithms can accurately predict PHQ-9 depression scale scores from NHANES data. The most influential audiometric predictors of higher scores on a validated depression scale were social dynamics of hearing loss and not objective audiometric testing. Such models could be useful in predicting depression scale scores at the point-of-care in conjunction with a standard audiologic assessment.