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Article

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

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Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia
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Research & Development Department, Nigerian Communications Commission, Abuja FCT 257776, Nigeria
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Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Anbar 31001, Iraq
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College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
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eVIDA Lab, University of Deusto, Avda/Universidades 24, 48007 Bilbao, Spain
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Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
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Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia
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Authors to whom correspondence should be addressed.
Academic Editors: Maxim A. Dulebenets, Zhiwu Li, Alireza Fallahpour and Amir M. Fathollahi-Fard
Sustainability 2021, 13(10), 5406; https://doi.org/10.3390/su13105406
Received: 19 March 2021 / Revised: 7 May 2021 / Accepted: 9 May 2021 / Published: 12 May 2021
Physicians 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. View Full-Text
Keywords: hearing-loss symptoms; frequent pattern growth; multivariate Bernoulli naïve Bayes; machine learning techniques; identification model hearing-loss symptoms; frequent pattern growth; multivariate Bernoulli naïve Bayes; machine learning techniques; identification model
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MDPI and ACS Style

Abd Ghani, M.K.; Noma, N.G.; Mohammed, M.A.; Abdulkareem, K.H.; Garcia-Zapirain, B.; Maashi, M.S.; Mostafa, S.A. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability 2021, 13, 5406. https://doi.org/10.3390/su13105406

AMA Style

Abd Ghani MK, Noma NG, Mohammed MA, Abdulkareem KH, Garcia-Zapirain B, Maashi MS, Mostafa SA. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability. 2021; 13(10):5406. https://doi.org/10.3390/su13105406

Chicago/Turabian Style

Abd Ghani, Mohd K., Nasir G. Noma, Mazin A. Mohammed, Karrar H. Abdulkareem, Begonya Garcia-Zapirain, Mashael S. Maashi, and Salama A. Mostafa. 2021. "Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques" Sustainability 13, no. 10: 5406. https://doi.org/10.3390/su13105406

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