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Rule based fuzzy logic approach for classification of fibromyalgia syndrome

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Abstract

Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual’s quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were evaluated for their conditions in other patient groups.

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Abbreviations

FMS:

Fibromyalgia syndrome

ACR:

American College of Rheumatology

VAS:

Visual analogue scale

TP:

Tender point count

CWP:

Chronic widespread pain duration

PS:

Pain severity

FS:

Fatigue severity

SD:

Sleep disturbance

FwLP:

Fibromyalgia with low probability (FibromyalgiaWLP)

FwMP:

Fibromyalgia with medium probability (FibromyalgiaWMP)

FwHP:

Fibromyalgia with high probability (FibromyalgiaWHP)

DF:

Definite fibromyalgia

SF:

Severe fibromyalgia

F^:

Not fibromyalgia

OD:

Other diagnosis (Chronic fatigue syndrome, Sleep disturbance, Depression or Local pain)

p:

Patient

c:

Control

i:

Imaginary subjects: random generated scores for testing the reliability of Fuzzy predictor around the boundary/critic scores

ACR C.:

American college of rheumatology criteria

S.:

Specialist

Ss.:

Specialists

P.:

Predictor

t-:

Total

BEO:

Between each other

N/A:

Not applicable

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Acknowledgments

This research is funded by TUBITAK within the context of the project code: 108E036, and titled “Evaluation of HRV, SSR and Psychological Tests using Wavelet Transform and Artificial Neural Nets for the diagnosis of Fibromyalgia Syndrome and Determination of Relations”. We would like to thank non-author contributors from our research group Suleyman Bilgin, Omer H. Colak, Onur Elmas, Ozhan Ozkan, Gurkan Bilgin, Seden Demirci, Hasan R. Koyuncuoglu, Selami Akkus, and Selcuk Comlekci.

Funding

This work was supported in part by the Scientific and Technological Research Council of Turkey under Grant 108E036.

Author Contributions

Study conception and design: E. Arslan, S. Yildiz, and E. Koklukaya

Acquisition of data: S. Yildiz

Analysis and interpretation of data: E. Arslan, and Y. Albayrak

Drafting of manuscript: E. Arslan, S. Yildiz, and Y. Albayrak

Critical revision: E. Arslan, S. Yildiz, and E. Koklukaya,

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey

    Evren Arslan

  2. Department of Physical Medicine and Rehabilitation, Egirdir Bone & Joint Disease Treatment and Rehabilitation Hospital, Isparta, Turkey

    Sedat Yildiz

  3. Department of Electrical and Electronics Engineering, Faculty of Engineering, Akdeniz University, Antalya, Turkey

    Yalcin Albayrak

  4. Sakarya University, Sakarya, Turkey

    Yalcin Albayrak & Etem Koklukaya

  5. Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey

    Etem Koklukaya

Authors
  1. Evren Arslan
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  2. Sedat Yildiz
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  3. Yalcin Albayrak
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  4. Etem Koklukaya
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Corresponding author

Correspondence to Yalcin Albayrak.

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Conflicts of Interest

None of the authors has a commercial interest, financial interest, and/or other relationship with manufacturers of pharmaceuticals, laboratory supplies, and/or medical devices or with commercial providers of medically related services.

Additional information

There is one prior IEEE conference proceeding paper available related with this work in Turkish language. The paper is available online at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5479745&tag=1.

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Arslan, E., Yildiz, S., Albayrak, Y. et al. Rule based fuzzy logic approach for classification of fibromyalgia syndrome. Australas Phys Eng Sci Med 39, 501–515 (2016). https://doi.org/10.1007/s13246-016-0452-z

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  • DOI: https://doi.org/10.1007/s13246-016-0452-z

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