Personalized Hearing Loss Care Using SNOMED CT-Aligned Ontology and Random Forest Machine Learning: A Hybrid Decision-Support Framework
This study demonstrates that combining SNOMED CT-aligned ontology with Random Forest classification achieves superior diagnostic accuracy and enables personalized treatment recommendations for hearing loss. The hybrid framework provides clinically interpretable decision support while ensuring semantic interoperability with electronic health records. Multi-institutional validation studies are necessary to assess generalizability across diverse populations before clinical deployment.
Authors
- Chamseddine Barki (ORCID: https://orcid.org/0000-0002-6595-3211)
- Nicola Luigi Bragazzi (ORCID: https://orcid.org/0000-0001-8409-868X)
- Hanene Boussi Rahmouni (ORCID: https://orcid.org/0000-0002-6282-4561)
- Halil İbrahim Ceylan (ORCID: https://orcid.org/0009-0005-2214-4667)
- Ismail Dergaa
- Darine Kebsi
- Riadh Gouider
- Mourad Elloumi
- Amina Maddouri
- Abderrazak Jemai
Institutions
- Tunis University (TN)
- York University (CA)
- Tunisia Polytechnic School (TN)
- University of Bisha (SA)
- SKF (France) (FR)
- Razi Hospital (IR)
- Faculté de médecine de Tunis
- University of Jendouba (TN)
- Tunis El Manar University (TN)
- Atatürk University (TR)
- Manouba University (TN)
Publication Details
- Journal
- Audiology Research
- Published
- 2026-03-02
- DOI
- https://doi.org/10.3390/audiolres16020037
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00