Innovative artificial intelligence approach for hearing-loss symptoms identification model using machine learning techniques

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dc.contributor.authorAbd Ghani, Mohd Khanapi
dc.contributor.authorNoma, Nasir G.
dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorAbdulkareem, Karrar Hameed
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorMaashi, Mashael S.
dc.contributor.authorMostafa, Salama A.
dc.date.accessioned2025-08-12T06:58:25Z
dc.date.available2025-08-12T06:58:25Z
dc.date.issued2021-05-12
dc.date.updated2025-08-12T06:58:25Z
dc.description.abstractPhysicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.en
dc.description.sponsorshipThis research received funding from Basque Country Government.en
dc.identifier.citationAbd Ghani, M. K., Noma, N. G., Mohammed, M. A., Abdulkareem, K. H., Garcia-Zapirain, B., Maashi, M. S., & Mostafa, S. A. (2021). Innovative artificial intelligence approach for hearing-loss symptoms identification model using machine learning techniques. Sustainability (Switzerland), 13(10). https://doi.org/10.3390/SU13105406
dc.identifier.doi10.3390/SU13105406
dc.identifier.eissn2071-1050
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3350
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2021 by the authors
dc.subject.otherFrequent pattern growth
dc.subject.otherHearing-loss symptoms
dc.subject.otherIdentification model
dc.subject.otherMachine learning techniques
dc.subject.otherMultivariate Bernoulli naïve Bayes
dc.titleInnovative artificial intelligence approach for hearing-loss symptoms identification model using machine learning techniquesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue10
oaire.citation.titleSustainability (Switzerland)
oaire.citation.volume13
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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