Predicting the outcomes of internet-based cognitive behavioral therapy for tinnitus: Applications of Artificial Neural Network and Support Vector Machine

Rodrigo, Hansapani, Beukes, Eldre, Andersson, Gerhard and Manchaiah, Vinaya (2022) Predicting the outcomes of internet-based cognitive behavioral therapy for tinnitus: Applications of Artificial Neural Network and Support Vector Machine. American Journal of Audiology. ISSN 1558-9137

Preview	Text Accepted Version Available under the following license: Creative Commons Attribution Non-commercial No Derivatives. Download (454kB) | Preview
	Text (Word Version) Accepted Version Available under the following license: Creative Commons Attribution Non-commercial No Derivatives. Download (114kB)
Preview	Text (Supplementary Material) Accepted Version Available under the following license: Creative Commons Attribution Non-commercial No Derivatives. Download (119kB) | Preview
Preview	Text (Supplementary Material) Accepted Version Available under the following license: Creative Commons Attribution Non-commercial No Derivatives. Download (290kB) | Preview

Abstract

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 in Tinnitus Functional Index (TFI) was defined as a successful outcome. There were 33 predictor variables, including demographic, tinnitus, hearing-related and treatment-related variables, and clinical factors (anxiety, depression, insomnia, hyperacusis, hearing disability, cognitive function, and life satisfaction). Predictive models using ANN and SVM were developed and evaluated for classification accuracy. SHapley Additive exPlanations (SHAP) analysis was used to identify the relative predictor variable importance using the best predictive model for a successful treatment outcome. Results: The best predictive model was achieved with the ANN with an average area under the receiver operating characteristic value of 0.73 ± 0.03. The SHAP analysis revealed that having a higher education level and a greater baseline tinnitus severity were the most critical factors that influence treatment outcome positively. Conclusions: Predictive models such as ANN and SVM help predict ICBT treatment outcomes and identify predictors of outcome. However, further work is needed to examine predictors that were not considered in this study as well as to improve the predictive power of these models.

Item Type:	Journal Article
Keywords:	tinnitus, Internet interventions, digital therapeutics, cognitive behavioral therapy, artificial intelligence, machine learning, artificial neural networks, support vector machines
Faculty:	Faculty of Science & Engineering
SWORD Depositor:	Symplectic User
Depositing User:	Symplectic User
Date Deposited:	03 Aug 2022 10:08
Last Modified:	02 Nov 2022 15:09
URI:	https://arro.anglia.ac.uk/id/eprint/707786

Actions (login required)

Edit Item