Next Article in Journal
Inspiriting Innovation: The Effects of Leader-Member Exchange (LMX) on Innovative Behavior as Mediated by Mindfulness and Work Engagement
Next Article in Special Issue
An Assessment of Social Distancing Obedience Behavior during the COVID-19 Post-Epidemic Period in China: A Cross-Sectional Survey
Previous Article in Journal
Voluntary Disclosure of GRI and CSR Environmental Criteria in Colombian Companies
Previous Article in Special Issue
Integrated Healthcare and the Dilemma of Public Health Emergencies
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Sustainability
Volume 13
Issue 10
10.3390/su13105406
sustainability-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

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

by
Mohd Khanapi Abd Ghani
1,
Nasir G. Noma
2,
Mazin Abed Mohammed
3,*,
Karrar Hameed Abdulkareem
4,
Begonya Garcia-Zapirain
5,*,
Mashael S. Maashi
6 and
Salama A. Mostafa
7
1
Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia
2
Research & Development Department, Nigerian Communications Commission, Abuja FCT 257776, Nigeria
3
Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Anbar 31001, Iraq
4
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
5
eVIDA Lab, University of Deusto, Avda/Universidades 24, 48007 Bilbao, Spain
6
Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
7
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia
*
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
(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)
View Full-Text Download PDF
Browse Figures

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. 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
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1

Author Biographies

Back to TopTop