Efficiency of Artificial Intelligence Methods for Hearing Loss Type Classification: an Evaluation

Abstract

The evaluation of hearing loss is primarily conducted by pure tone audiometry testing, which is often regarded as golden standard for assessing auditory function. If the presence of hearing loss is determined, it is possible to differentiate between three types of hearing loss: sensorineural, conductive, and mixed. This study presents a comprehensive comparison of a variety of AI classification models, performed on 4007 pure tone audiometry samples that have been labeled by professional audiologists in order to develop an automatic classifier of hearing loss type. The tested models include Logistic Regression, Support Vector Machines, Stochastic Gradient Descent, Decision Trees, Random Forest, Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The presented work also investigates the influence of training dataset augmentation with the use of a Conditional Generative Adversarial Network on the performance of machine learning algorithms and examines the impact of various standardization procedures on the effectiveness of deep learning architectures. Overall, the highest classification performance, was achieved by LSTM with an out-of-training accuracy of 97.56%.