In this paper a neural network is described that designed to provide aid in the preventive diagnosis of hearing loss issues. Hearing loss is a widely widespread disability affecting millions of people worldwide. An anonymous dataset is used to train a neural network to evaluate hearing loss in prevention and early diagnosis with the aim of supporting health care by optimising time and cost. The system is tested using a second set of data and results in a correct evaluation of whether the patient is affected by hearing loss or not.
Authors: Eugenio Donati (University of West London), Christos Chousidis (University of West London) and Samuele Calabrese (University of West London)
