Automated Hearing Impairment Diagnosis Using Machine Learning

Abstract

Approximately 700 million people will suffer from disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with incapacitating hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time. In this paper, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of the automated hearing impairment diagnosis software, which consists of two modules: a hearing test Data Generation Module and a Machine Learning Model. The Data Generation Module produces a diverse and exhaustive dataset for training and evaluating the Machine Learning Model. By employing multiclass and multi-label classification techniques to learn from the hearing test data, the model can instantaneously predict the type, degree, and configuration of hearing loss with high accuracy. Our proposed Machine Learning Model demonstrates propitious results with a prediction time of 634 ms, a log-loss reduction rate of 98.48%, and macro and micro precisions of 100%—showing the model’s applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss.