Predicting hearing aids outcomes using machine learning.

Abstract

Introduction The aim of this study is to measure the effectiveness of hearing aid fitting in improving understanding in quiet and in noise, and to investigate the factors that significantly influence these results. This study will be carried out through a retrospective analysis of the results obtained from patients fitted with hearing aids at hearing-aid centers between 2018 and 2021. This study explores and classifies the predictive factors of patient outcomes, looking at the impact of the choice of hearing aid technology (category level), the personalized adjustments made by the hearing care professional (amplification level, binaural loudness balancing) and the patient follow-up and daily use (data logging). Methods Hearing impaired people were fitted in hearing-aid centers from 2018 to 2021. This included 77,661 patients. In the first part of the study, the data are statistically analyzed, and various correlations are studied. Then, machine learning and feature explanatory algorithms are used for prediction, in particular, a neural network based on PyTorch and the eXtreme Gradient Boosting (XGBoost). For explanatory power, the SHapley Additive exPlanation (SHAP) method was used to evaluate the individual contribution of each variable. Results Using predictive models (SHAP value and XGBoost), the effect of technology level on Speech Perception In Noise (SPIN) and Speech Perception In Quiet (SPIQ) scores is significant, and binaural loudness compensation is significant for improving test results by the same amount. Finally, linear relationships were found between the initial Speech Noise Ratio (SNR) and the adjusted SNR, as well as for the Speech Recognition Threshold (SRT). Finally, the effect of wearing hearing aids for more than 9 hours per day was analyzed and resulted in better recovery. This is a retrospective study. This bias is compensated by the large amount of data. Conclusion Big data analysis is a new method to evaluate predictive factors for hearing aid outcomes.