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

Mohd Khanapi Abd Ghani, Nasir G. Noma, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Begonya Garcia-Zapirain, Mashael S. Maashi and Salama A. Mostafa
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Mohd Khanapi Abd Ghani: Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia
Nasir G. Noma: Research & Development Department, Nigerian Communications Commission, Abuja FCT 257776, Nigeria
Mazin Abed Mohammed: Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Anbar 31001, Iraq
Karrar Hameed Abdulkareem: College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
Begonya Garcia-Zapirain: eVIDA Lab, University of Deusto, Avda/Universidades 24, 48007 Bilbao, Spain
Mashael S. Maashi: Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Salama A. Mostafa: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia

Sustainability, 2021, vol. 13, issue 10, 1-30

Abstract: 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.

Keywords: hearing-loss symptoms; frequent pattern growth; multivariate Bernoulli naïve Bayes; machine learning techniques; identification model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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