IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i10p5406-d553059.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mohd Khanapi Abd Ghani & Nasir G. Noma & Mazin Abed Mohammed & Karrar Hameed Abdulkareem & Begonya Garcia-Zapirain & Mashael S. Maashi & Salama A. Mostafa, 2021. "Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques," Sustainability, MDPI, vol. 13(10), pages 1-30, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5406-:d:553059
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/10/5406/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/10/5406/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soumya Raychaudhuri & Robert M Plenge & Elizabeth J Rossin & Aylwin C Y Ng & International Schizophrenia Consortium & Shaun M Purcell & Pamela Sklar & Edward M Scolnick & Ramnik J Xavier & David Altsh, 2009. "Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions," PLOS Genetics, Public Library of Science, vol. 5(6), pages 1-15, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yehong Liu & Xin Wang & Dong Dai & Can Tang & Xu Mao & Du Chen & Yawei Zhang & Shumao Wang, 2023. "Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage," Agriculture, MDPI, vol. 13(7), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benjamin Lehne & Cathryn M Lewis & Thomas Schlitt, 2011. "From SNPs to Genes: Disease Association at the Gene Level," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-10, June.
    2. Gavin Band & Quang Si Le & Luke Jostins & Matti Pirinen & Katja Kivinen & Muminatou Jallow & Fatoumatta Sisay-Joof & Kalifa Bojang & Margaret Pinder & Giorgio Sirugo & David J Conway & Vysaul Nyirongo, 2013. "Imputation-Based Meta-Analysis of Severe Malaria in Three African Populations," PLOS Genetics, Public Library of Science, vol. 9(5), pages 1-13, May.
    3. Alexandra Zhernakova & Eli A Stahl & Gosia Trynka & Soumya Raychaudhuri & Eleanora A Festen & Lude Franke & Harm-Jan Westra & Rudolf S N Fehrmann & Fina A S Kurreeman & Brian Thomson & Namrata Gupta &, 2011. "Meta-Analysis of Genome-Wide Association Studies in Celiac Disease and Rheumatoid Arthritis Identifies Fourteen Non-HLA Shared Loci," PLOS Genetics, Public Library of Science, vol. 7(2), pages 1-13, February.
    4. Kerstin Becker & Sabine Siegert & Mohammad Reza Toliat & Juanjiangmeng Du & Ramona Casper & Guido H Dolmans & Paul M Werker & Sigrid Tinschert & Andre Franke & Christian Gieger & Konstantin Strauch & , 2016. "Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytr," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    5. Michael A Nalls & David J Couper & Toshiko Tanaka & Frank J A van Rooij & Ming-Huei Chen & Albert V Smith & Daniela Toniolo & Neil A Zakai & Qiong Yang & Andreas Greinacher & Andrew R Wood & Melissa G, 2011. "Multiple Loci Are Associated with White Blood Cell Phenotypes," PLOS Genetics, Public Library of Science, vol. 7(6), pages 1-16, June.
    6. Xiaofeng Dai & Wenwen Guo & Chunjun Zhan & Xiuxia Liu & Zhonghu Bai & Yankun Yang, 2015. "WDR5 Expression Is Prognostic of Breast Cancer Outcome," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-15, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5406-:d:553059. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.