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Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Personalized Hearing Loss Care Using SNOMED CT-Aligned Ontology and Random Forest Machine Learning: A Hybrid

Darine Kebsi1, Chamseddine Barki1, Ismail Dergaa2,3

  • 1Research Laboratory of Biophysics and Medical Technologies, The Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, 9, Street Z. Essafi, Tunis 1006, Tunisia.

Audiology Research
|March 24, 2026
PubMed
Summary

This study developed an ontology-based machine learning model to accurately classify hearing loss types and personalize treatment. The approach significantly improved diagnostic accuracy and clinical interpretability for hearing loss management.

Keywords:
Random ForestSNOMED CTartificial intelligencehearing lossmachine learningmedical informaticsontologypersonalized medicinesemantic reasoning

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Audiology

Background:

  • Hearing loss affects over 466 million people globally and is a significant Alzheimer's disease risk factor.
  • Current hearing loss diagnostics and treatments lack personalization due to complex causes.
  • Integrating medical ontologies with machine learning offers enhanced diagnostic accuracy and personalized treatment.

Purpose of the Study:

  • Develop a Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT)-aligned clinical ontology for hearing loss.
  • Implement a Random Forest classifier for accurate hearing loss type classification (conductive, sensorineural, mixed, normal).
  • Predict personalized hearing loss treatments using patient data including laterality, severity, audiometric thresholds, and medical history.

Main Methods:

  • Developed a task ontology using Protégé and Web Ontology Language (OWL), aligned with SNOMED CT.
  • Utilized Semantic Web Rule Language (SWRL) and Pellet reasoner for automated reasoning.
  • Trained and evaluated Random Forest models on 3723 adult patients from the NHANES dataset (2015-2016) with comparative analysis against K-Means clustering.

Main Results:

  • The ontology successfully classified hearing loss types, severity, and laterality for all patients.
  • Ontology-enriched data improved Random Forest model accuracy to 92.48% (vs. 90.2% with K-Means), with improved F1-scores across all classes.
  • Audiometric thresholds, ontology-derived severity, and medical history were key predictors, enhancing clinical interpretability.

Conclusions:

  • Combining a SNOMED CT-aligned ontology with Random Forest classification enhances hearing loss diagnosis and enables personalized treatment recommendations.
  • The hybrid framework offers clinically interpretable decision support and semantic interoperability with electronic health records.
  • Further multi-institutional validation is required to assess generalizability before clinical deployment.