Hearing impairment is one of the most common sensory disorders affecting newborns, infants, and young children worldwide. Early detection of hearing loss is crucial because delayed diagnosis can negatively affect speech development, cognitive growth, social interaction, and educational outcomes in children. However, many developing and underdeveloped regions face a shortage of audiologists and otolaryngologists, which often results in delayed diagnosis and limited access to hearing care services. This situation highlights the need for automated and intelligent diagnostic tools that can assist healthcare professionals in identifying hearing impairments more efficiently. This study proposes an automated hearing loss detection framework based on machine learning techniques to support medical professionals in diagnosing hearing impairments in newborns, infants, and toddlers. The proposed system integrates a hearing test data generation module with a machine learning classification model capable of analyzing audiometry test data and predicting the presence and characteristics of hearing loss. The data generation module creates a comprehensive dataset representing different hearing conditions, which is then used to train and evaluate the machine learning model. By employing multiclass and multi-label classification techniques, the model can identify the type, degree, and configuration of hearing loss with high accuracy. Experimental results demonstrate strong diagnostic performance, achieving a prediction time of approximately 634 milliseconds, a log-loss reduction rate of 98.48%, and macro and micro precision values close to 100%. These results indicate that the proposed framework can provide rapid and reliable diagnostic support for healthcare professionals, enabling earlier intervention and improving access to hearing care in regions with limited medical resources.
Chamundeswari et al. (Thu,) studied this question.