Development of a Predictive Model for Individualized Hearing Aid Benefit.

Abstract

To develop a model to predict individualized hearing aid benefit. To provide interpretations of model predictions on global and individual levels. We compiled a data set of patients with hearing loss who trialed hearing aids and completed the Client Oriented Scale of Improvement (COSI) questionnaire, a validated patient-reported outcome measure of hearing aid benefit. Features included demographic, medical, and audiological measures. The outcome was the COSI score for change in listening ability with hearing aids, scaled from 1 to 5. Model development was performed using fivefold cross-validation repeated three times with hyperparameter tuning. Model performance was assessed using the root mean squared error (RMSE) of the COSI scores. Model interpretation was performed using Shapley Additive Explanations. The data set comprised 1,286 patients across 3,523 listening situations. The best performing model was random forest with an RMSE of 0.80, found to be significantly better than the next best model (eXtreme gradient boosting with RMSE of 0.85, p < 0.01). The most important features in predicting hearing aid benefit were shorter duration of hearing aid use, higher pure-tone average in the better hearing ear, and younger age. We have developed a predictive model for hearing aid benefit that can also provide individualized explanations of model predictions. Predictive modeling could be a useful tool in assessing a patient's candidacy and predicted benefit from hearing aids.