Abstract
The evaluation of hearing loss is primarily conducted by pure tone audiometry testing, which is often regarded as the gold standard for assessing auditory function. This method enables the detection of hearing impairment, which may be further identified as conductive, sensorineural, or 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 random forest, support vector machines, logistic regression, stochastic gradient descent, decision trees, convolutional neural network (CNN), feedforward neural network (FNN), recurrent neural network (RNN), gated recurrent unit (GRU) and long short-term memory (LSTM). 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%.