Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques
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 othe...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Sustainability |
Subjects: | |
Online Access: | https://www.mdpi.com/2071-1050/13/10/5406 |
id |
doaj-a1269178d33d406a9c468830afefca66 |
---|---|
record_format |
Article |
spelling |
doaj-a1269178d33d406a9c468830afefca662021-05-31T23:48:58ZengMDPI AGSustainability2071-10502021-05-01135406540610.3390/su13105406Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning TechniquesMohd Khanapi Abd Ghani0Nasir G. Noma1Mazin Abed Mohammed2Karrar Hameed Abdulkareem3Begonya Garcia-Zapirain4Mashael S. Maashi5Salama A. Mostafa6Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka 76100, MalaysiaResearch & Development Department, Nigerian Communications Commission, Abuja FCT 257776, NigeriaInformation Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Anbar 31001, IraqCollege of Agriculture, Al-Muthanna University, Samawah 66001, IraqeVIDA Lab, University of Deusto, Avda/Universidades 24, 48007 Bilbao, SpainSoftware Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, MalaysiaPhysicians 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.https://www.mdpi.com/2071-1050/13/10/5406hearing-loss symptomsfrequent pattern growthmultivariate Bernoulli naïve Bayesmachine learning techniquesidentification model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohd Khanapi Abd Ghani Nasir G. Noma Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain Mashael S. Maashi Salama A. Mostafa |
spellingShingle |
Mohd Khanapi Abd Ghani Nasir G. Noma Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain Mashael S. Maashi Salama A. Mostafa Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques Sustainability hearing-loss symptoms frequent pattern growth multivariate Bernoulli naïve Bayes machine learning techniques identification model |
author_facet |
Mohd Khanapi Abd Ghani Nasir G. Noma Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain Mashael S. Maashi Salama A. Mostafa |
author_sort |
Mohd Khanapi Abd Ghani |
title |
Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques |
title_short |
Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques |
title_full |
Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques |
title_fullStr |
Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques |
title_full_unstemmed |
Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques |
title_sort |
innovative artificial intelligence approach for hearing-loss symptoms identification model using machine learning techniques |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-05-01 |
description |
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. |
topic |
hearing-loss symptoms frequent pattern growth multivariate Bernoulli naïve Bayes machine learning techniques identification model |
url |
https://www.mdpi.com/2071-1050/13/10/5406 |
work_keys_str_mv |
AT mohdkhanapiabdghani innovativeartificialintelligenceapproachforhearinglosssymptomsidentificationmodelusingmachinelearningtechniques AT nasirgnoma innovativeartificialintelligenceapproachforhearinglosssymptomsidentificationmodelusingmachinelearningtechniques AT mazinabedmohammed innovativeartificialintelligenceapproachforhearinglosssymptomsidentificationmodelusingmachinelearningtechniques AT karrarhameedabdulkareem innovativeartificialintelligenceapproachforhearinglosssymptomsidentificationmodelusingmachinelearningtechniques AT begonyagarciazapirain innovativeartificialintelligenceapproachforhearinglosssymptomsidentificationmodelusingmachinelearningtechniques AT mashaelsmaashi innovativeartificialintelligenceapproachforhearinglosssymptomsidentificationmodelusingmachinelearningtechniques AT salamaamostafa innovativeartificialintelligenceapproachforhearinglosssymptomsidentificationmodelusingmachinelearningtechniques |
_version_ |
1721416475556708352 |