Skip to main content

Advertisement

SpringerLink
Log in

Pythagorean fuzzy cognitive analysis for medical care and treatment decisions

  • Original Paper
  • Published:
Granular Computing Aims and scope Submit manuscript

Abstract

The medical industry has employed a variety of decision-making methods to help medical professionals. The use of fuzzy techniques is necessitated by the fact that medical data is often ambiguous. This study desires to show the decision-making application of a novel Pythagorean Fuzzy Cognitive Map (PFCM) in the treatment of pregnant women with heart disease. The PFCM integrates the principles of Pythagorean fuzzy sets with cognitive maps, resulting in a better intuitive model for human understanding. PFCM combines Pythagorean Fuzzy TOPSIS and Fuzzy Cognitive Maps, determining weights for expert opinions and criteria. It yields a fuzzy cognitive map with weighted linkages to visualize relationship strengths. To measure the impact of the PFCM, we conduct a hypothetical case study in which women were assumed to have cardiovascular disease. We gathered input values, diagnosis, and prognosis data and used them to design an algorithm that demonstrates the complete working of the system. After completing the algorithm, we validate the model using some example values and compared the accuracy obtained with other techniques. Our findings show that the PFCM is a highly accurate and effective tool for decision-making in the treatment of pregnant women with heart disease. The present study offers new insights into the use of Pythagorean fuzzy cognitive maps and their potential for improving decision-making in healthcare.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

No data were used to support this study.

References

  • Abdullah L, Goh P (2019) Decision making method based on Pythagorean fuzzy sets and its application to solid waste management. Complex Intell Syst 5:185–198

    Article  Google Scholar 

  • Akram M, Zahid S (2023) Group decision-making method with Pythagorean fuzzy rough number for the evaluation of best design concept. Granul Comput. https://doi.org/10.1007/s41066-023-00391-0

    Article  Google Scholar 

  • Akram M, Dudek WA, Ilyas F (2019) Group decision-making based on pythagorean fuzzy TOPSIS method. Int J Intell Syst 34(7):1455–1475

    Article  Google Scholar 

  • Akram M, Shahzadi G, Ahmadini AAH (2020) Decision-making framework for an effective sanitizer to reduce COVID-19 under Fermatean fuzzy environment. J Math 2020:1–19. https://doi.org/10.1155/2020/3263407

    Article  MathSciNet  MATH  Google Scholar 

  • Akram M, Habib A, Allahviranloo T (2022) A new maximal flow algorithm for solving optimization problems with linguistic capacities and flows. Inf Sci 612:201–230

    Article  Google Scholar 

  • Akram M, Ramzan N, Deveci M (2023a) Linguistic Pythagorean fuzzy CRITIC-EDAS method for multiple-attribute group decision analysis. Eng Appl Artif Intell 119:105777. https://doi.org/10.1016/j.engappai.2022.105777

    Article  Google Scholar 

  • Akram M, Bibi R, Deveci M (2023b) An outranking approach with 2-tuple linguistic Fermatean fuzzy sets for multi-attribute group decision-making. Eng Appl Artif Intell 121:105992. https://doi.org/10.1016/j.engappai.2023.105992

    Article  Google Scholar 

  • Aldring J, Ajay D (2023) Multicriteria group decision making based on projection measures on complex Pythagorean fuzzy sets. Granul Comput 8(1):137–155

    Article  Google Scholar 

  • Al-subhi SH, Rubio PAR, Perez PP, Vacacela RG, Mahdi GSS (2020) Neutrosophic clinical decision support system for the treatment of pregnant women with heart diseases. Investig Oper 41(5):773–783

    Google Scholar 

  • Axelrod R (ed) (2015) Structure of decision: the cognitive maps of political elites. Princeton University Press, Princeton

    Google Scholar 

  • Babroudi NEP, Sabri-Laghaie K, Ghoushchi NG (2021) Re-evaluation of the healthcare service quality criteria for the COVID-19 pandemic: Z-number fuzzy cognitive map. Appl Soft Comput 112:107775. https://doi.org/10.1016/j.asoc.2021.107775

    Article  Google Scholar 

  • Boyaci AC, Şişman A (2022) Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets. Environ Sci Pollut Res 29(2):1985–1997

    Article  Google Scholar 

  • Brandl L, van Velsen L, Brodbeck J, Jacinto S, Hofs D, Heylen D (2023) Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps. Digit Health 9:20552076231183548

    Google Scholar 

  • Çalık A (2021) A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Comput 25(3):2253–2265

    Article  Google Scholar 

  • Chen SM, Jong WT (1997) Fuzzy query translation for relational database systems. IEEE Trans Syst Man Cybern Part B (Cybern) 27(4):714–721

    Article  Google Scholar 

  • Chen SM, Cheng SH, Lan TC (2016) Multicriteria decision making based on the TOPSIS method and similarity measures between intuitionistic fuzzy values. Inf Sci 367:279–295

    Article  Google Scholar 

  • Cheng S, Chan CW, Huang GH (2003) An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management. Eng Appl Artif Intell 16(5–6):543–554

    Article  Google Scholar 

  • Chi P, Liu P (2013) An extended TOPSIS method for the multiple attribute decision making problems based on interval neutrosophic set. Neutrosophic Sets Syst 1(1):63–70

    Google Scholar 

  • Chu TC (2002) Facility location selection using fuzzy TOPSIS under group decisions. Int J Uncertain Fuzziness Knowl Based Syst 10(06):687–701

    Article  MathSciNet  MATH  Google Scholar 

  • Darko AP, Liang D (2020) Some q-rung orthopair fuzzy Hamacher aggregation operators and their application to multiple attribute group decision making with modified EDAS method. Eng Appl Artif Intell 87:103259. https://doi.org/10.1016/j.engappai.2019.103259

    Article  Google Scholar 

  • Diaz DRB, Lopez LRR, Castro LPA (2020) Neutrosophic DEMATEL to prioritize risk factors in teenage pregnancy. Neutrosophic Sets Syst 37:24–30. http://fs.unm.edu/NSS2/index.php/111/article/view/838/611

    Google Scholar 

  • Ejegwa PA (2020a) Distance and similarity measures for Pythagorean fuzzy sets. Granul Comput 5(2):225–238. https://doi.org/10.1007/s41066-018-00149-z

    Article  MathSciNet  Google Scholar 

  • Ejegwa PA (2020b) Improved composite relation for Pythagorean fuzzy sets and its application to medical diagnosis. Granul Comput 5(2):277–286

    Article  MathSciNet  Google Scholar 

  • Ejegwa PA, Awolola JA (2021) Novel distance measures for Pythagorean fuzzy sets with applications to pattern recognition problems. Granul Comput 6(1):181–189

    Article  Google Scholar 

  • Garg H, Shahzadi G, Akram M (2020) Decision-making analysis based on Fermatean fuzzy Yager aggregation operators with application in COVID-19 testing facility. Math Probl Eng 2020:1–16. https://doi.org/10.1155/2020/7279027

    Article  MathSciNet  MATH  Google Scholar 

  • Habib S, Akram M (2018) Diagnostic methods and risk analysis based on fuzzy soft information. Int J Biomath 11(08):1850096. https://doi.org/10.1142/S1793524518500961

    Article  MathSciNet  MATH  Google Scholar 

  • Habib S, Akram M (2019) Medical decision support systems based on fuzzy cognitive maps. Int J Biomath 12(06):1950069. https://doi.org/10.1142/S1793524519500694

    Article  MathSciNet  MATH  Google Scholar 

  • Habib S, Butt MA, Akram M, Smarandache F (2020) A neutrosophic clinical decision-making system for cardiovascular diseases risk analysis. J Intell Fuzzy Syst 39(5):7807–7829

    Article  Google Scholar 

  • Habib A, Akram M, Kahraman C (2022) Minimum spanning tree hierarchical clustering algorithm: a new Pythagorean fuzzy similarity measure for the analysis of functional brain networks. Expert Syst Appl 201:117016

    Article  Google Scholar 

  • Haktanır E, Kahraman C (2022) A novel picture fuzzy CRITIC & REGIME methodology: wearable health technology application. Eng Appl Artif Intell 113:104942. https://doi.org/10.1016/j.engappai.2022.104942

    Article  Google Scholar 

  • Joudar SS, Albahri AS, Hamid RA (2023) Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods. Inform Med Unlocked 36:101131. https://doi.org/10.1016/j.imu.2022.101131

    Article  Google Scholar 

  • Kandasamy WBV, Smarandache F (2003) Fuzzy cognitive maps and neutrosophic cognitive maps. Xiquan Phoenix, AZ, p 213

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

    Article  MATH  Google Scholar 

  • Kumar K, Chen SM (2023) Group decision making based on entropy measure of Pythagorean fuzzy sets and Pythagorean fuzzy weighted arithmetic mean aggregation operator of Pythagorean fuzzy numbers. Inf Sci 624:361–377

    Article  Google Scholar 

  • Liu P, Rani P, Mishra AR (2021) A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology. J Clean Prod 292:126047. https://doi.org/10.1016/j.jclepro.2021.126047

    Article  Google Scholar 

  • Obot O, John A, Udo I, Attai K, Johnson E, Udoh S, Uzoka FM (2023) Modelling differential diagnosis of febrile diseases with fuzzy cognitive map. Trop Med Infect Dis 8(7):352

    Article  Google Scholar 

  • Pan L, Gao X, Deng Y, Cheong KH (2021) Constrained Pythagorean fuzzy sets and its similarity measure. IEEE Trans Fuzzy Syst 30(4):1102–1113

    Article  Google Scholar 

  • Pregnancy and heart disease (2019) ACOG. https://www.acog.org/clinical/clinical-guidance/practice-bulletin/articles/2019/05/pregnancy-and-heart-disease. Accessed 18 Apr 2023

  • Rani P, Chen SM, Mishra AR (2023) Multiple attribute decision making based on MAIRCA, standard deviation-based method, and Pythagorean fuzzy sets. Info Sci 644:119274

    Article  Google Scholar 

  • Regitz-Zagrosek V, Kruger J, Sliwa K (2021) Aortic and valvular heart diseases, cardiomyopathies and heart failure in pregnancy: risk assessment and management. Herz 46(4):385–396

    Article  Google Scholar 

  • Rotshtein A, Pustylnik L, Katielnikov D (2021) Fuzzy cognitive maps in reliability modeling. In: Advancements in fuzzy reliability theory. IGI Global, Seoul, pp 1–19

  • Suluba E, Shuwei L, Xia Q, Mwanga A (2020) Congenital heart diseases: genetics, non-inherited risk factors, and signaling pathways. Egypt J Med Hum Genet 21(1):1–12

    Article  Google Scholar 

  • Taylor K, Elhakeem A, Thorbjornsrud Nader JL, Yang TC, Isaevska E, Richiardi L, Lawlor DA (2021) Effect of maternal prepregnancy/early-pregnancy body mass index and pregnancy smoking and alcohol on congenital heart diseases: a parental negative control study. J Am Heart Assoc 10:e020051. https://doi.org/10.1161/JAHA.120.020051

    Article  Google Scholar 

  • Verma R, Merigó JM (2019) On generalized similarity measures for Pythagorean fuzzy sets and their applications to multiple attribute decision-making. Int J Intell Syst 34(10):2556–2583

    Article  Google Scholar 

  • Verma R, Mittal A (2023) Multiple attribute group decision-making based on novel probabilistic ordered weighted cosine similarity operators with Pythagorean fuzzy information. Granul Comput 8(1):111–129

    Article  Google Scholar 

  • Wang K, Feng G, Shi Q, Zeng S (2023) An entropy-GRA-TOPSIS model for evaluating the quality of enterprises’ green information disclosure from the perspective of green financing. Granul Comput. https://doi.org/10.1007/s41066-023-00401-1

    Article  Google Scholar 

  • Xu TT, Zhang H, Li BQ (2020) Pythagorean fuzzy entropy and its application in multiple-criteria decision-making. Int J Fuzzy Syst 22:1552–1564

    Article  Google Scholar 

  • Yager RR (2013) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  • Zhang Z, Chen SM (2022) Group decision making based on multiplicative consistency and consensus of Pythagorean fuzzy preference relations. Inf Sci 601:340–356

    Article  Google Scholar 

  • Zhang X, Xu Z (2014) Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. Int J Intell Syst 29(12):1061–1078

    Article  MathSciNet  Google Scholar 

  • Zhu Y, Gu J, Chen W, Luo D, Zeng S (2023) Multiple attribute decision-making based on a prospect theory-based TOPSIS method for venture capital selection with complex information. Granul Comput. https://doi.org/10.1007/s41066-023-00398-7

    Article  Google Scholar 

Download references

Funding

There is no specific funding for this project

Author information

Authors and Affiliations

  1. School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

    Shaista Habib

  2. Division of Science and Technology, Department of Mathematics, University of Education, Lahore, Pakistan

    Sundas Shahzadi

  3. Department of Industrial Engineering, Turkish Naval Academy, National Defence University, 34940, Tuzla, Istanbul, Turkey

    Muhammet Deveci

  4. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Muhammet Deveci

Authors
  1. Shaista Habib
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sundas Shahzadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammet Deveci
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SH: concept, design, analysis, and writing of the manuscript. SS: concept, design, analysis, writing, or revision of the manuscript. MD: concept, design, analysis, writing, or revision of the manuscript.

Corresponding author

Correspondence to Muhammet Deveci.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

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

Habib, S., Shahzadi, S. & Deveci, M. Pythagorean fuzzy cognitive analysis for medical care and treatment decisions. Granul. Comput. 8, 1887–1906 (2023). https://doi.org/10.1007/s41066-023-00407-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41066-023-00407-9

Keywords

Search

Navigation