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This paper proposes Artificial Intelligence as a transformative tool for the predictive analysis of hearing loss within intelligent medicine. We aim to refine the predictive accuracy for sensorineural hearing loss outcomes by harnessing Artificial Intelligence capabilities. The research encapsulates the systematic organization of qualitative features within a meticulously curated database crafted with the guidance of audiology experts. This database is then employed to train AI-based models, thereby enabling the nuanced interpretation of complex patterns and relationships inherent in the data. The goal is to advance the predictive methodologies that can inform and enhance hearing loss diagnosis and treatment strategies. The code and database generated for this work have been released as a contribution.

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Exploratory Analysis in the Intelligent Prediction of Sensorineural Hearing Loss Using Qualitative Features

  • 会议论文

  • 发表日期：

    2025年

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  • Rene Barahona,
  • Bruno Retamal,
  • Javiera Leon,
  • Diego Montenegro,
  • Ignacio Bugueno-Cordova,
  • Alfonso Ehijo

摘要

This paper proposes Artificial Intelligence as a transformative tool for the predictive analysis of hearing loss within intelligent medicine. We aim to refine the predictive accuracy for sensorineural hearing loss outcomes by harnessing Artificial Intelligence capabilities. The research encapsulates the systematic organization of qualitative features within a meticulously curated database crafted with the guidance of audiology experts. This database is then employed to train AI-based models, thereby enabling the nuanced interpretation of complex patterns and relationships inherent in the data. The goal is to advance the predictive methodologies that can inform and enhance hearing loss diagnosis and treatment strategies. The code and database generated for this work have been released as a contribution.