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Abstract

Hearing loss or hearing impairment is one of the most leading cause highly affecting the people around the world in present days. Additionally, this disease primarily affects children and adults, which has an impact on their daily lives, careers, education, and other aspects of their lives. Therefore, it has to be correctly identified and diagnosed so that early therapies can be givento save people's lives. The many kinds of automated hearingloss prediction/detection systems are created for this aim in conventional works. The majority of the current research focuses on applying prediction techniques based on machine learning and deeplearning to diagnose diseases. Its shortcomings include challenging computational procedures, longer training and testing times, greater mis-prediction outcomes, and incorrect outputs. Therefore, the proposed work objects to develop a Human Age -Hearing Impairment & Level (HAHIL) prediction system by using the machine learning methodologies. In this study,we describe a computer-aided strategy to forecast hearing loss and then prevent it. Here, three distinct prediction models are deployed for age prediction, hearing loss detection, and its severity level prediction. The Biased Probability Neural Network (BPNN) technique is utilized to predict the age based on simulated human acoustical signals. Then, the Regularized Extreme Learning Machine (RELM) mechanism is deployed for predicting the hearing impairment by constructing the weight and target matrices. During evaluation, the performance of the proposed HAHIL prediction system is validated and tested by using various evaluation indicators.The effectiveness of our computer-aided hearing loss prediction methods has been proven to be very high and they can be applied to the actual application system.Also, it provides an increased accuracy up to 99%, which is highlysuperior than the existing prediction models.

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