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April 17, 2026Open Access

A Machine Learning-Based Automated System For Early Detection And Classification Of Hearing Loss In Infants And Toddlers

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Automated system detects and classifies hearing loss in infants, suggesting improved diagnosis in resource-limited settings.

This research aims to develop an automated system for early detection and classification of hearing loss in infants and toddlers.
Proposed an automated hearing loss detection framework using machine learning techniques.
Integrated a hearing test data generation module with a classification model.
Analyzed audiometry test data to predict hearing impairment characteristics.
Employed multiclass and multi-label classification techniques for accurate identification.
Achieved a prediction time of approximately 634 milliseconds.
Log-loss reduction rate of 98.48%.
Macro and micro precision values close to 100%.
Demonstrated strong diagnostic performance, supporting earlier intervention.

Chamundeswari et al. (2026) studied this question.

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