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Fuzzy Models Applied to Medical Diagnosis: A Systematic Review

Received: 20 October 2019     Accepted: 21 November 2019     Published: 27 November 2019
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Abstract

Fuzzy logic lies in the ability to process nonlinear relationships. Because of the clinical complexity and pathologic heterogeneity of various diseases, correct identification of patients with active disease likely depends on the presence of a single defining feature. Hence, it is not surprising that standard linear statistical methodologies are relatively inadequate for medical diagnosis. In the medical field, dealing with diagnosis error and several levels of uncertainties and imprecision in the diagnoses of diseases had been a great challenge. To solve such problems, artificial intelligence gives a solution through expert system. Fuzzy Logic handles uncertainties, imprecisions and obscurity in decision making. Fuzzy logic is been preferred by Researchers because of its flexible structure and use of intuitive methods instead of specific algorithm. It deals with the degree of membership as it refers to the extent to which an event occurred or can occur. Fuzzy set uses the continuum of logical values between 0 and 1. Different Fuzzy models were reviewed. These systems diagnose many diseases such as: Malaria born infectious disease, Heart related diseases or cardiovascular diseases (like Atherosclerosis), cancer, Asthma, Lungs cancer, Cold and Flu, Hepatitis, Osteomyelitis and Meningitis. In the near future, medical service delivery will be more accessible and more efficient due to availability of Medical Diagnostic Systems.

Published in Advances in Networks (Volume 7, Issue 2)
DOI 10.11648/j.net.20190702.15
Page(s) 45-50
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Artificial Intelligence, Fuzzy Logic, Medical Diagnosis

References
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Cite This Article
  • APA Style

    Labiga Laban Thomas, Ibrahim Goni, Gideon Daniel Emeje. (2019). Fuzzy Models Applied to Medical Diagnosis: A Systematic Review. Advances in Networks, 7(2), 45-50. https://doi.org/10.11648/j.net.20190702.15

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    ACS Style

    Labiga Laban Thomas; Ibrahim Goni; Gideon Daniel Emeje. Fuzzy Models Applied to Medical Diagnosis: A Systematic Review. Adv. Netw. 2019, 7(2), 45-50. doi: 10.11648/j.net.20190702.15

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    AMA Style

    Labiga Laban Thomas, Ibrahim Goni, Gideon Daniel Emeje. Fuzzy Models Applied to Medical Diagnosis: A Systematic Review. Adv Netw. 2019;7(2):45-50. doi: 10.11648/j.net.20190702.15

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  • @article{10.11648/j.net.20190702.15,
      author = {Labiga Laban Thomas and Ibrahim Goni and Gideon Daniel Emeje},
      title = {Fuzzy Models Applied to Medical Diagnosis: A Systematic Review},
      journal = {Advances in Networks},
      volume = {7},
      number = {2},
      pages = {45-50},
      doi = {10.11648/j.net.20190702.15},
      url = {https://doi.org/10.11648/j.net.20190702.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.net.20190702.15},
      abstract = {Fuzzy logic lies in the ability to process nonlinear relationships. Because of the clinical complexity and pathologic heterogeneity of various diseases, correct identification of patients with active disease likely depends on the presence of a single defining feature. Hence, it is not surprising that standard linear statistical methodologies are relatively inadequate for medical diagnosis. In the medical field, dealing with diagnosis error and several levels of uncertainties and imprecision in the diagnoses of diseases had been a great challenge. To solve such problems, artificial intelligence gives a solution through expert system. Fuzzy Logic handles uncertainties, imprecisions and obscurity in decision making. Fuzzy logic is been preferred by Researchers because of its flexible structure and use of intuitive methods instead of specific algorithm. It deals with the degree of membership as it refers to the extent to which an event occurred or can occur. Fuzzy set uses the continuum of logical values between 0 and 1. Different Fuzzy models were reviewed. These systems diagnose many diseases such as: Malaria born infectious disease, Heart related diseases or cardiovascular diseases (like Atherosclerosis), cancer, Asthma, Lungs cancer, Cold and Flu, Hepatitis, Osteomyelitis and Meningitis. In the near future, medical service delivery will be more accessible and more efficient due to availability of Medical Diagnostic Systems.},
     year = {2019}
    }
    

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    T1  - Fuzzy Models Applied to Medical Diagnosis: A Systematic Review
    AU  - Labiga Laban Thomas
    AU  - Ibrahim Goni
    AU  - Gideon Daniel Emeje
    Y1  - 2019/11/27
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    N1  - https://doi.org/10.11648/j.net.20190702.15
    DO  - 10.11648/j.net.20190702.15
    T2  - Advances in Networks
    JF  - Advances in Networks
    JO  - Advances in Networks
    SP  - 45
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    PB  - Science Publishing Group
    SN  - 2326-9782
    UR  - https://doi.org/10.11648/j.net.20190702.15
    AB  - Fuzzy logic lies in the ability to process nonlinear relationships. Because of the clinical complexity and pathologic heterogeneity of various diseases, correct identification of patients with active disease likely depends on the presence of a single defining feature. Hence, it is not surprising that standard linear statistical methodologies are relatively inadequate for medical diagnosis. In the medical field, dealing with diagnosis error and several levels of uncertainties and imprecision in the diagnoses of diseases had been a great challenge. To solve such problems, artificial intelligence gives a solution through expert system. Fuzzy Logic handles uncertainties, imprecisions and obscurity in decision making. Fuzzy logic is been preferred by Researchers because of its flexible structure and use of intuitive methods instead of specific algorithm. It deals with the degree of membership as it refers to the extent to which an event occurred or can occur. Fuzzy set uses the continuum of logical values between 0 and 1. Different Fuzzy models were reviewed. These systems diagnose many diseases such as: Malaria born infectious disease, Heart related diseases or cardiovascular diseases (like Atherosclerosis), cancer, Asthma, Lungs cancer, Cold and Flu, Hepatitis, Osteomyelitis and Meningitis. In the near future, medical service delivery will be more accessible and more efficient due to availability of Medical Diagnostic Systems.
    VL  - 7
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer Science, Faculty of Physical Science, Modibbo Adama University of Technology, Yola, Nigeria

  • Department of Computer Science, Faculty of Science, Adamawa State University, Mubi, Nigeria

  • Department of Computer Science, Faculty of Physical Science, Modibbo Adama University of Technology, Yola, Nigeria

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