Skip to main content
Log in

Similarity measurement of fuzzy entropies of respiratory sounds and risk measurement according to credibility distributions

  • Fuzzy systems and their mathematics
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In this paper, we have provided some definitions and theorems about fuzzy set, fuzzy set operations, the uncertainty theory used in fuzzy environments, and the credibility theory which exists on the base of the mathematical knowledge. Then, the definition and applications of fuzzy set operations, fuzzification, defuzzification, similarity, credibility distribution were included in diagnostic medicine. Sounds of respiratory patients were transferred to computer by recording and converted into numerical data. In particular, the entropies, similarities, credibility.distributions., expected values of sounds were analyzed using fuzzification and defuzzification methods to analyze respiratory sound data via fuzzy operations and to make sense of uncertain sound data. As the uncertainty of respiratory sounds increased, changes in fuzzy entropy similarity measures and expected credibility values were observed. In addition, a model for uncertain respiratory sounds was created and numerical numbers supported the diagnosis of the physician. Sound data were analyzed and interpreted using fuzzy multipurpose decision methods. Respiratory sounds were shown to be an indicator in diagnosing with the created model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

References

  • Adlassnig K-P (1986) Fuzzy set theory in medical diagnosis. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/TSMC.1986.4308946

    Article  Google Scholar 

  • Barnabas B (2013) Studies in fuzziness and soft computing. Springer, Heidelberg, New York

    Google Scholar 

  • Cao BY, Yang JH, Zhou XG, Kheiri Z, Zahmatkesh F, Yang XP (2020) Fuzzy relational mathematical programming. Springer, Switzerland

    Book  Google Scholar 

  • Chaudhuri A, Ghosh SK (2016) Studies in fuzziness and soft computing. Springer, Cham Hedidelberg, New York, Dordrecht London

    Google Scholar 

  • De Luca A, Termini S (1972a) A definition of a non-probalistic entropy in the setting of fuzzy sets. Inf Control 20:301–312

    Article  Google Scholar 

  • Dwyer S. J. (2000) A personalized view of the history of PACS in the USA. In: Proceedings of the SPIE, medical imaging 2000, PACS design and evaluation: engineering and clinical issues. 3980: 2–9

  • Fraiwan M, Fraiwan L, Alkhodari M (2021) Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-03184-y

    Article  Google Scholar 

  • Jayalakshmy S, Sudha GF (2020) Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks. Artif Intell Med 103:101809

    Article  Google Scholar 

  • Kolmogorov AN (1933) Grundbegriffe der wahrscheinlichkeitsrechnung. Julius Springer, Berlin

    Book  Google Scholar 

  • Li Y, He X, Qin K (2016) Relationships among several fuzzy measures. In: Handbook of fuzzy sets comparison 6: 43–78

  • Li X, Ralescu DA (2009) Credibility measure of fuzzy sets and applications. Int J Adv Intell Paradig. https://doi.org/10.1504/IJAIP.2009.026567

    Article  Google Scholar 

  • Liu B (2015) Uncertainty theory, 5th edn. Springer, Berlin

    MATH  Google Scholar 

  • Liu YH, Ha MH (2010) Expected value of function of uncertain variables. J Uncertain Syst 4(3):181–186

    Google Scholar 

  • Liu B, Liu YK (2002) Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2002.800692

    Article  MATH  Google Scholar 

  • Lowen R (1980) Fuzzy sets and systems. An international journal in information science and engineering, Elsevier 3(3), 291–310

  • Luca AD, Termini SA (1972b) Definition of non probabilistic entropy in the setting of fuzzy set theory. Inf Control 20:301–312

    Article  Google Scholar 

  • Mason RJ, Murray JF (2010) Murray and nadel’s textbook of respiratory medicine, 5th edn. Saunders Elsevier, Philadelphia

    Google Scholar 

  • Pasterkamp H, Kraman SS, Wodicka GR (1997) Respiratory sounds – advances beyond the stethoscope. Am J Respir Crit Care Med 156:974–987

    Article  Google Scholar 

  • Pelletier FJ (2000) Review of metamathematics of fuzzy logics. Bull Symb Log 6(3):342–346. https://doi.org/10.2307/421060

    Article  Google Scholar 

  • Sanlıbaba I (2019) Fuzzy set theory and applications in medical diagnosis of some diseases. Nevşehir

  • Sengonul M, Kayaduman K, Zararsız Z, Atpınar S (2016) The entropies of the sequences of fuzzy sets and applications of entropy to cardiography. Int J Math Modell Comput 3:159–173

    Google Scholar 

  • Shi L, Du K, Zhang C, Ma H, Yan W (2019) Lung sound recognition algorithm based on vggish-bigru. IEEE Access 7:139438–139449

    Article  Google Scholar 

  • Sovijarvi A, Dalmasso F, Vanderschoot J, Malmberg L, Righini G, Stoneman S (2000) Definition of terms for applications of respiratory sounds. Eur Respir Rev 10(77):597–610

    Google Scholar 

  • Sridevi B, Nadarajan R (2009) Fuzzy similarity measure for generalized fuzzy numbers. Int J Open Problems Compt Math 2:240–253

    MathSciNet  MATH  Google Scholar 

  • Yuanguo Z (2019) Uncertain optimal control. Springer Uncertainty Research, China

    MATH  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  • Zadeh L (1971) Similarity relations and fuzzy orderings. Inf Sci 3:177–200

    Article  MathSciNet  Google Scholar 

  • Zadeh L (1965) Probability measures of fuzzy events. J Math Anal 29:49–55, 1989, Appl., 23:421–427

  • Zimmermann H-J (1991) Fuzzy set theory- its applications, Second Revised Edition. Kluwer Academic Publishers, USA

    Book  Google Scholar 

  • Zimmermann HJ (1996) Fuzzy set theory and its applications. Kluwer Academic Publishers, Netherland

    Book  Google Scholar 

Download references

Funding

No funds available.

Author information

Authors and Affiliations

Authors

Contributions

In this study, I would like to thank Uzeyir ÇİMEN for recording respiratory sounds, Haydar ÜNSAL for computer drawings, and Nevzat KALAY for English translation.

Corresponding author

Correspondence to İbrahim Şanlıbaba.

Ethics declarations

Conflict of interest

I. Şanlıbaba declares that he has no conflict of interest.

Ethical Approval

No Potential conflicts of interest and is an article with a single author. There are human data whose names are unknown. The data were created by giving numbers to people, respectively. Therefore, there is no need for any approval.

Informed consent

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Şanlıbaba, İ. Similarity measurement of fuzzy entropies of respiratory sounds and risk measurement according to credibility distributions. Soft Comput 26, 10007–10017 (2022). https://doi.org/10.1007/s00500-022-07415-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07415-y

Keywords

Navigation