Physical Sciences and Mathematics Commons^™

Explainable Machine Learning Reveals The Relationship Between Hearing Thresholds And Speech-In-Noise Recognition In Listeners With Normal Audiograms, Jithin Raj Balan, Hansapani Rodrigo, Udit Saxena, Srikanta K. Mishra

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Some individuals complain of listening-in-noise difficulty despite having a normal audiogram. In this study, machine learning is applied to examine the extent to which hearing thresholds can predict speech-in-noise recognition among normal-hearing individuals. The specific goals were to (1) compare the performance of one standard (GAM, generalized additive model) and four machine learning models (ANN, artificial neural network; DNN, deep neural network; RF, random forest; XGBoost; eXtreme gradient boosting), and (2) examine the relative contribution of individual audiometric frequencies and demographic variables in predicting speech-in-noise recognition. Archival data included thresholds (0.25–16 kHz) and speech recognition thresholds (SRTs) from listeners with …

Hearing Healthcare Professionals’ Views About Over-The-Counter (Otc) Hearing Aids: Analysis Of Retrospective Survey Data, Vinaya Manchaiah, Anu Sharma, Hansapani Rodrigo, Abram Bailey, Karina C. De Sousa, De Wet Swanepoel

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Over-the-counter hearing aids have been available to consumers in the US since 17 October 2022 following a ruling by the Food and Drug Administration. However, their reception by hearing healthcare professionals (HHP) has been mixed, and concerns have been expressed by many HHPs. The aim of this study was to examine the concerns that HHPs have towards over-the-counter (OTC) hearing aids. The study used a retrospective survey design. The survey data of HHPs (n = 730) was obtained from Hearing Tracker. A 22-item structured questionnaire was administered using a Question Scout platform. Descriptive analyses examined reported areas of concern and …

Predicting The Outcomes Of Internet-Based Cognitive Behavioral Therapy For Tinnitus: Applications Of Artificial Neural Network And Support Vector Machine, Hansapani Rodrigo, Eldré W. Beukes, Gerhard Andersson, Vinaya Manchaiah

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Purpose:

Internet-based cognitive behavioral therapy (ICBT) has been found to be effective for tinnitus management, although there is limited understanding about who will benefit the most from ICBT. Traditional statistical models have largely failed to identify the nonlinear associations and hence find strong predictors of success with ICBT. This study aimed at examining the use of an artificial neural network (ANN) and support vector machine (SVM) to identify variables associated with treatment success in ICBT for tinnitus.

Method:

The study involved a secondary analysis of data from 228 individuals who had completed ICBT in previous intervention studies. A 13-point reduction …