Skip to main content

Advertisement

Springer Nature Link
Log in

Development of a multi-stage fuzzy cognitive map for an uncertainty environment: methods and introduction

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Today, hospital failures become a challenging issue regarding their occasional irreparable consequences. Many patients lose their lives each year due to medical failures, with their costs estimated at millions of dollars. Therefore, paying attention to them and correct management of these failures can save the patients’ life. In this regard, various methods have been introduced for evaluating and ranking them. Failure Mode and Effects Analysis (FMEA) in combination with the Gray Relational Analysis (GRA) is among the most effective methods for evaluating these failures. The present study aims to evaluate patient operating failures by considering such failures in an uncertain environment and considering causal relations between these failures. For this purpose, a new method is proposed, which is an extension of the fuzzy cognitive map. This method, called multi-stage fuzzy gray cognitive map, evaluates the operating room failures more accurately by considering the values obtained from FMEA as input, as well as the causal relationship between the failures identified by the medical expert team. The proposed cognitive map was trained by two network training methods including delta rule extended for the multi-stage fuzzy gray cognitive map and Nonlinear Hebbian learning (NHL) algorithm. One of the most important limitations of the present study was the lack of sufficient research on the subject under study and also the difficulty of selecting the appropriate case to validate the proposed model. The results obtained from ranking with the proposed approach and comparing it with previous methods showed more accurate ranking and elimination of duplicates at one level of ranking. These results can be a strong reference for management team decisions in future for better management of hospital failures.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Groumpos PP (2010) Fuzzy cognitive maps: basic theories and their application to complex systems. In: Fuzzy cognitive maps Springer, Berlin, Heidelberg, pp 1–22

  2. Salmeron JL (2010) Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Syst Appl 37(12):7581–7588

    Article  Google Scholar 

  3. Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75

    Article  MATH  Google Scholar 

  4. Liu S, Forrest JYL (2010) Grey systems: theory and applications. Springer, Berlin

    Book  Google Scholar 

  5. Papageorgiou EI, Subramanian J, Karmegam A, Papandrianos N (2015) A risk management model for familial breast cancer: a new application using Fuzzy Cognitive Map method. Comput Methods Programs Biomed 122(2):123–135

    Article  Google Scholar 

  6. Stach W, Kurgan L, Pedrycz W (2010) Expert-based and computational methods for developing fuzzy cognitive maps. In: Fuzzy cognitive maps. Springer, Berlin, Heidelberg, pp 23–41

  7. Papageorgiou EI (2010) A novel approach on constructed dynamic fuzzy cognitive maps using fuzzified decision trees and knowledge-extraction techniques. In: Fuzzy cognitive maps. Springer, Berlin, Heidelberg, pp 43–70

  8. Rezaee MJ, Yousefi S, Babaei M (2017) Multi-stage cognitive map for failures assessment of production processes: an extension in structure and algorithm. Neurocomputing 232:69–82

    Article  Google Scholar 

  9. Jahangoshai Rezaee M, Yousefi S, Hayati J (2018) A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level. Neural Comput Appl 29(3):761–777. https://doi.org/10.1007/s00521-016-2478-2

    Article  Google Scholar 

  10. Amirkhani A, Papageorgiou EI, Mosavi MR, Mohammadi K (2018) A novel medical decision support system based on fuzzy cognitive maps enhanced by intuitive and learning capabilities for modeling uncertainty. Appl Math Comput 337:562–582

    Google Scholar 

  11. Georgopoulos VC, Stylios CD (2013) Fuzzy cognitive map decision support system for successful triage to reduce unnecessary emergency room admissions for the elderly. In: Fuzziness and medicine: philosophical reflections and application systems in health care. Springer, Berlin, Heidelberg, pp 415–436

  12. Papageorgiou EI (2012) Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection. Comput Methods Programs Biomed 105(3):233–245. https://doi.org/10.1016/j.cmpb.2011.09.006

    Article  Google Scholar 

  13. Alipour M, Hafezi R, Amer M, Akhavan AN (2017) A new hybrid fuzzy cognitive map-based scenario planning approach for Iran’s oil production pathways in the post–sanction period. Energy 135:851–864

    Article  Google Scholar 

  14. Amer M, Daim T, Jetter A (2013) Application of fuzzy cognitive map for the development of scenarios: a case study of Wind Energy Deployment. In: Research and technology management in the electricity industry. Springer, London, pp 129–159

  15. Rezaee MJ, Yousefi S, Hayati J (2019) Root barriers management in development of renewable energy resources in Iran: An interpretative structural modeling approach. Energy Policy 129:292–306

    Article  Google Scholar 

  16. Vidal R, Salmeron JL, Mena A, Chulvi V (2015) Fuzzy cognitive Map-based selection of TRIZ (theory of inventive problem solving) trends for eco-innovation of ceramic industry products. J Clean Prod 107:202–214

    Article  Google Scholar 

  17. Rezaee MJ, Yousefi S, Eshkevari M, Valipour M, Saberi M (2020) Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA. Stoch Environ Res Risk Assess 34(1):201–218

    Article  Google Scholar 

  18. Salmeron JL, Ruiz-Celma A, Mena A (2017) Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes. Neurocomputing 232:52–57

    Article  Google Scholar 

  19. Lee KC, Lee H, Lee N, Lim J (2013) An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms. Ind Mark Manag 42(4):552–563

    Article  Google Scholar 

  20. Dogu E, Albayrak YE (2018) Criteria evaluation for pricing decisions in strategic marketing management using an intuitionistic cognitive map approach. Soft Comput 22(15):4989–5005

    Article  Google Scholar 

  21. Azar A, Dolatabad KM (2019) A method for modelling operational risk with fuzzy cognitive maps and Bayesian belief networks. Expert Syst Appl 115:607–617

    Article  Google Scholar 

  22. Senniappan V, Subramanian J, Papageorgiou EI, Mohan S (2017) Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures. Neural Comput Appl 28(1):107–117

    Article  Google Scholar 

  23. Salmeron JL, Gutierrez E (2012) Fuzzy grey cognitive maps in reliability engineering. Appl Soft Comput 12(12):3818–3824

    Article  Google Scholar 

  24. Bezuglov A, Comert G (2016) Short-term freeway traffic parameter prediction: Application of grey system theory models. Expert Syst Appl 62:284–292

    Article  Google Scholar 

  25. Wang S, Wang P, Zhang Y (2020) A prediction method for urban heat supply based on grey system theory. Sustain Cities Soc 52:101819

    Article  Google Scholar 

  26. Wang W, Liu X, Qin Y, Fu Y (2018) A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral. Saf Sci 110:152–163

    Article  Google Scholar 

  27. Papageorgiou EI, Salmeron JL (2012) A review of fuzzy cognitive maps research during the last decade. IEEE Trans Fuzzy Syst 21(1):66–79

    Article  Google Scholar 

  28. Papageorgiou EI, Salmeron JL (2012) Learning fuzzy grey cognitive maps using nonlinear hebbian-based approach. Int J Approx Reason 53(1):54–65

    Article  MATH  Google Scholar 

  29. Amirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR (2017) A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. Comput Methods Programs Biomed 142:129–145

    Article  Google Scholar 

  30. Bevilacqua M, Ciarapica FE, Mazzuto G (2018) Fuzzy cognitive maps for adverse drug event risk management. Saf Sci 102:194–210

    Article  Google Scholar 

  31. Rezaee MJ, Yousefi S, Valipour M, Dehdar MM (2018) Risk analysis of sequential processes in food industry integrating multi-stage fuzzy cognitive map and process failure mode and effects analysis. Comput Ind Eng 123:325–337

    Article  Google Scholar 

  32. Liu HC (2019) Improved FMEA methods for proactive healthcare risk analysis. Springer, Singapore

    Book  Google Scholar 

  33. Liu HC (2019) FMEA for proactive healthcare risk analysis: a systematic literature review, In: Improv. FMEA methods proactive Healthc. Risk Anal., pp. 15–45

  34. Chang K-H, Chang Y-C, Tsai I-T (2013) Enhancing FMEA assessment by integrating grey relational analysis and the decision making trial and evaluation laboratory approach. Eng Fail Anal 31:211–224

    Article  Google Scholar 

  35. Salmeron JL, Palos-Sanchez PR (2017) Uncertainty propagation in fuzzy grey cognitive maps with Hebbian-like learning algorithms. IEEE Trans Cybern 49(1):211–220

    Article  Google Scholar 

  36. Dağsuyu C, Göçmen E, Narlı M, Kokangül A (2016) Classical and fuzzy FMEA risk analysis in a sterilization unit. Comput Ind Eng 101:286–294

    Article  Google Scholar 

  37. Weerakkody RA et al (2013) Surgical technology and operating-room safety failures: a systematic review of quantitative studies. BMJ Qual Saf 22(9):710–718

    Article  Google Scholar 

  38. Salmeron JL, Mansouri T, Moghadam MRS, Mardani A (2019) Learning fuzzy cognitive maps with modified asexual reproduction optimisation algorithm. Knowledge-Based Syst 163:723–735

    Article  Google Scholar 

  39. Jahangoshai Rezaee M, Yousefi S, Valipour M, Dehdar MM (2018) Risk analysis of sequential processes in food industry integrating multi-stage fuzzy cognitive map and process failure mode and effects analysis. Comput. Ind. Eng. 123:325–337. https://doi.org/10.1016/j.cie.2018.07.012

    Article  Google Scholar 

  40. Natarajan R, Subramanian J, Papageorgiou EI (2016) Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Comput Electron Agric 127:147–157. https://doi.org/10.1016/j.compag.2016.05.016

    Article  Google Scholar 

  41. Abbaspour Onari M, Yousefi S, Jahangoshai Rezaee M (2021) Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm. Artif. Intell. Rev. 54(2):1349–1383

    Article  Google Scholar 

  42. Petalas YG, Parsopoulos KE, Vrahatis MN (2009) Improving fuzzy cognitive maps learning through memetic particle swarm optimization. Soft Comput 13(1):77–94

    Article  Google Scholar 

Download references

Acknowledgments

The data of the present study were collected and evaluated with the approval of an expert team consisting of general surgeons from Urmia University of Medical Sciences. We would like to thank Dr. Mehdi Javaheri.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran

    Sohrab Abdollahzadeh & Jamileh Hayati

Authors
  1. Sohrab Abdollahzadeh
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jamileh Hayati
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Sohrab Abdollahzadeh.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships (direct or indirect) that could have appeared to influence the work reported in this paper.

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 (e.g. a society or other partner) 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

Abdollahzadeh, S., Hayati, J. Development of a multi-stage fuzzy cognitive map for an uncertainty environment: methods and introduction. Neural Comput & Applic 35, 4499–4517 (2023). https://doi.org/10.1007/s00521-022-07778-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07778-1

Keywords