Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options

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

Abstract

Selecting medical treatments is a critical activity in medical decision-making. Usually, medical treatments are selected by doctors, patients, and their families based on various criteria. Due to the subjectivity of decision-making and the large volume of information available, accurately and comprehensively evaluating information with traditional fuzzy sets is impractical. Interval neutrosophic linguistic numbers (INLNs) can be effectively used to evaluate information during the medical treatment selection process. In this study, a medical treatment selection method based on prioritized harmonic mean operators in an interval neutrosophic linguistic environment, in which criteria and decision-makers are assigned different levels of priority, is developed. First, the rectified linguistic scale functions of linguistic variables, new INLN operations, and an INLN comparison method are developed in order to prevent data loss and distortion during the aggregation process. Next, a generalized interval neutrosophic linguistic prioritized weighted harmonic mean operator and a generalized interval neutrosophic linguistic prioritized hybrid harmonic mean operator are developed in order to aggregate the interval neutrosophic linguistic information. Then, these operators are used to develop an interval neutrosophic linguistic multi-criteria group decision-making method. In addition, the proposed method is applied to a practical treatment selection method. Furthermore, the ranking results are compared to those obtained using a traditional approach in order to confirm the practicality and accuracy of the proposed method.

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

Access this article

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.

Fig. 1

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Liberatore MJ, Nydick RL (2008) The analytic hierarchy process in medical and health care decision making: a literature review. Eur J Oper Res 189(1):194–207

    Article  MATH  Google Scholar 

  2. Ijzerman MJ, Van Til JA, Bridges JF (2012) A comparison of analytic hierarchy process and conjoint analysis methods in assessing treatment alternatives for stroke rehabilitation. Patient Patient Centered Outcomes Res 5(1):45–56

    Article  Google Scholar 

  3. Yuen KKF (2014) The Primitive cognitive network process in healthcare and medical decision making: comparisons with the analytic hierarchy process. Appl Soft Comput 14:109–119

    Article  Google Scholar 

  4. Hummel JM, Bridges JF, IJzerman MJ (2014) Group decision making with the analytic hierarchy process in benefit-risk assessment: a tutorial. Patient Patient Centered Outcomes Res 7(2):129–140

    Article  Google Scholar 

  5. Moreno E, Girón FJ, Vázquez-Polo FJ, Negrín MA (2012) Optimal healthcare decisions: the importance of the covariates in cost–effectiveness analysis. Eur J Oper Res 218(2):512–522

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen TY, Chang CH, Lu JR (2013) The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making. Eur J Oper Res 226(3):615–625

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  8. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  MathSciNet  MATH  Google Scholar 

  9. Torra V, Narukawa Y (2009) On hesitant fuzzy sets and decision. In: The 18th IEEE international conference on fuzzy systems in fuzzy systems. pp 1378–1382

  10. Smarandache F (1999) A unifying field in logics. Neutrosophy: neutrosophic probability, set and logic. American Research Press, Rehoboth

    MATH  Google Scholar 

  11. Smarandache F (2013) Introduction to neutrosophic measure, neutrosophic integral, and neutrosophic probability. Sitech & Education Publisher, Craiova-Columbus

    MATH  Google Scholar 

  12. Pramanik S, Mondal K (2015) Cosine similarity measure of rough neutrosophic sets and its application in medical diagnosis. Glob J Adv Res 2(1):212–220

    Google Scholar 

  13. Ye J (2015) Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med 63(3):171–179

    Article  Google Scholar 

  14. Broumi S, Deli I, Smarandache F (2015) N-valued interval neutrosophic sets and their application in medical diagnosis. Crit Rev 10:46–69

    Google Scholar 

  15. Deli I, Broumi S, Smarandache F (2015) On neutrosophic refined sets and their applications in medical diagnosis. J New Theory 6:88–98

    Google Scholar 

  16. Biswas P, Pramanik S, Giri BC (2014) A new methodology for neutrosophic multi-attribute decision-making with unknown weight information. Neutrosophic Sets Syst 3:42–50

    Google Scholar 

  17. Broumi S, Ye J, Smarandache F (2015) An extended TOPSIS method for multiple attribute decision making based on interval neutrosophic uncertain linguistic variables. Neutrosophic Sets Syst 8:23–32

    Google Scholar 

  18. Eisa M (2014) A new approach for enhancing image retrieval using neutrosophic sets. Int J Comput Appl 95(8):12–20

    Google Scholar 

  19. Guo YH, Şengür A, Tian JW (2016) A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Comput Methods Programs Biomed 123:43–53

    Article  Google Scholar 

  20. Zhang ZM, Wu C (2014) A novel method for single-valued neutrosophic multi-criteria decision making with incomplete weight information. Neutrosophic Sets Syst 4:35–49

    Google Scholar 

  21. Wang HB, Smarandache F, Zhang YQ, Sunderraman R (2010) Single valued neutrosophic sets. Multispace Multistruct 4:410–413

    MATH  Google Scholar 

  22. Wang HB, Smarandache F, Sunderraman R, Zhang YQ (2005) Interval neutrosophic sets and logic: theory and applications in computing. Hexis, Phoenix

    MATH  Google Scholar 

  23. Peng JJ, Wang JQ, Wu XH, Wang J, Chen XH (2015) Multi-valued neutrosophic sets and power aggregation operators with their applications in multi-criteria group decision-making problems. Int J Comput Intell Syst 8(2):345–363

    Article  Google Scholar 

  24. Liu PD, Teng F (2015) Multiple attribute decision making method based on normal neutrosophic generalized weighted power averaging operator. Int J Mach Learn Cybernet. doi:10.1007/s13042-015-0385-y

    Google Scholar 

  25. Deli I, Broumi S (2015) Neutrosophic soft matrices and NSM-decision making. J Intell Fuzzy Syst 28(5):2233–2241

    Article  MathSciNet  MATH  Google Scholar 

  26. Ye J (2015) Trapezoidal neutrosophic set and its application to multiple attribute decision-making. Neural Comput Appl 26(5):1157–1166

    Article  Google Scholar 

  27. Ye J (2015) Multiple-attribute decision-making method under a single-valued neutrosophic hesitant fuzzy environment. J Intell Syst 24(1):23–36

    Google Scholar 

  28. Ye J (2014) A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. J Intell Fuzzy Syst 26(5):2459–2466

    MathSciNet  MATH  Google Scholar 

  29. Liu PD, Wang YM (2014) Multiple attribute decision-making method based on single-valued neutrosophic normalized weighted Bonferroni mean. Neural Comput Appl 25(7–8):2001–2010

    Article  Google Scholar 

  30. Liu PD, Chu YC, Li YW, Chen YB (2014) Some generalized neutrosophic number Hamacher aggregation operators and their application to group decision making. Int J Fuzzy Syst 16(2):242–255

    Google Scholar 

  31. Peng JJ, Wang JQ, Wang J, Zhang HY, Chen XH (2014) Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. Int J Syst Sci. doi:10.1080/00207721.2014.994050

    MATH  Google Scholar 

  32. Ye J (2015) Single-valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine. Soft Comput. doi:10.1007/s00500-015-1818-y

    Google Scholar 

  33. Ye J (2014) Improved correlation coefficients of single valued neutrosophic sets and interval neutrosophic sets for multiple attribute decision making. J Intell Fuzzy Syst 27(5):2453–2462

    MATH  Google Scholar 

  34. Peng JJ, Wang J, Zhang HY, Chen XH (2014) An outranking approach for multi-criteria decision-making problems with simplified neutrosophic sets. Appl Soft Comput 25:336–346

    Article  Google Scholar 

  35. Biswas P, Pramanik S, Giri BC (2015) TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Comput Appl. doi:10.1007/s00521-015-1891-2

    Google Scholar 

  36. Şahin R, Liu PD (2015) Maximizing deviation method for neutrosophic multiple attribute decision making with incomplete weight information. Neural Comput Appl. doi:10.1007/s00521-015-1995-8

    Google Scholar 

  37. Broumi S, Smarandache F (2015) New operations on interval neutrosophic sets. J N Theory 1:24–37

    Google Scholar 

  38. Liu PD, Shi LL (2015) The generalized hybrid weighted average operator based on interval neutrosophic hesitant set and its application to multiple attribute decision making. Neural Comput Appl 26(2):457–471

    Article  Google Scholar 

  39. Broumi S, Smarandache F (2015) Interval-valued neutrosophic soft rough sets. Int J Comput Math. doi:10.1155/2015/232919

    MathSciNet  Google Scholar 

  40. Deli I (2015) Interval-valued neutrosophic soft sets and its decision making. Int J Mach Learn Cybernet. doi:10.1007/s13042-015-0461-3

    Google Scholar 

  41. Sun HX, Yang HX, Wu JZ, Yao OY (2015) Interval neutrosophic numbers Choquet integral operator for multi-criteria decision making. J Intell Fuzzy Syst 28(6):2443–2455

    Article  MathSciNet  MATH  Google Scholar 

  42. Ye J (2015) Multiple attribute decision-making method based on the possibility degree ranking method and ordered weighted aggregation operators of interval neutrosophic numbers. J Intell Fuzzy Syst 28(3):1307–1317

    MathSciNet  Google Scholar 

  43. Liu PD, Wang YM (2015) Interval neutrosophic prioritized OWA operator and its application to multiple attribute decision making. J Syst Sci Complex. doi:10.1007/s11424-015-4010-7

    MATH  Google Scholar 

  44. Ye J (2014) Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making. J Intell Fuzzy Syst 26(1):165–172

    MATH  Google Scholar 

  45. Zhang HY, Ji P, Wang J, Chen XH (2015) An improved weighted correlation coefficient based on integrated weight for interval neutrosophic sets and its application in multi-criteria decision-making problems. Int J Comput Intell Syst 8(6):1027–1043

    Article  Google Scholar 

  46. Tian ZP, Zhang HY, Wang J, Wang JQ, Chen XH (2015) Multi-criteria decision-making method based on a cross-entropy with interval neutrosophic sets. Int J Syst Sci. doi:10.1080/00207721.2015.1102359

    MATH  Google Scholar 

  47. Zhang HY, Wang J, Chen XH (2015) An outranking approach for multi-criteria decision-making problems with interval-valued neutrosophic sets. Neural Comput Appl. doi:10.1007/s00521-015-1882-3

    Google Scholar 

  48. Martínez L, Da R, Herrera F, Herrera-Viedma E, Wang PP (2009) Linguistic decision making: tools and applications. Inf Sci 179:2297–2298

    Article  Google Scholar 

  49. Wang JQ, Wu JT, Wang J, Zhang HY, Chen XH (2014) Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems. Inf Sci 288:55–72

    Article  MathSciNet  MATH  Google Scholar 

  50. Merigó JM, Casanovas M, Martínez L (2010) Linguistic aggregation operators for linguistic decision making based on the Dempster–Shafer theory of evidence. Int J Uncertain Fuzziness Knowl Based Syst 18(3):287–304

    Article  MathSciNet  MATH  Google Scholar 

  51. Merigó JM, Gil-Lafuente AM (2013) Induced 2-tuple linguistic generalized aggregation operators and their application in decision-making. Inf Sci 236:1–16

    Article  MathSciNet  MATH  Google Scholar 

  52. Tian ZP, Wang J, Wang JQ, Chen XH (2015) Multi-criteria decision-making approach based on gray linguistic weighted Bonferroni mean operator. Int Trans Oper Res. doi:10.1111/itor.12220

    Google Scholar 

  53. Wang J, Wang JQ, Zhang HY, Chen XH (2016) Multi-criteria group decision making approach based on 2-tuple linguistic aggregation operators with multi-hesitant fuzzy linguistic information. Int J Fuzzy Syst 18(1):81–97

    Article  MathSciNet  Google Scholar 

  54. Ye J (2015) An extended TOPSIS method for multiple attribute group decision making based on single valued neutrosophic linguistic numbers. J Intell Fuzzy Syst 28(1):247–255

    MathSciNet  Google Scholar 

  55. Ye J (2014) Some aggregation operators of interval neutrosophic linguistic numbers for multiple attribute decision making. J Intell Fuzzy Syst 27(5):2231–2241

    MathSciNet  MATH  Google Scholar 

  56. Broumi S, Smarandache F (2014) Single valued neutrosophic trapezoid linguistic aggregation operators based multi-attribute decision making. Bull Pure Appl Sci Math Stat 33(2):135–155

    Article  Google Scholar 

  57. Tian ZP, Wang J, Zhang HY, Chen XH, Wang JQ (2015) Simplified neutrosophic linguistic normalized weighted Bonferroni mean operator and its application to multi-criteria decision-making problems. Filomat. doi:10.2298/FIL1508576F

    MATH  Google Scholar 

  58. Yager RR (2008) Prioritized aggregation operators. Int J Approx Reason 48(1):263–274

    Article  MathSciNet  MATH  Google Scholar 

  59. Delgado M, Verdegay JL, Vila MA (1992) Linguistic decision-making models. Int J Intell Syst 7(5):479–492

    Article  MATH  Google Scholar 

  60. Xu ZS (2004) A method based on linguistic aggregation operators for group decision making with linguistic preference relation. Inf Sci 166:19–30

    Article  MathSciNet  MATH  Google Scholar 

  61. Xu ZS (2008) Group decision making based on multiple types of linguistic preference relations. Inf Sci 178:452–467

    Article  MathSciNet  MATH  Google Scholar 

  62. Xu ZS (2009) Fuzzy harmonic mean operators. Int J Intell Syst 24(2):152–172

    Article  MATH  Google Scholar 

  63. Wang JQ, Wu JT, Wang J, Zhang HY, Chen XH (2015) Multi-criteria decision-making methods based on the Hausdorff distance of hesitant fuzzy linguistic numbers. Soft Comput. doi:10.1007/s00500-015-1609-5

    Google Scholar 

  64. Zhou H, Wang J, Zhang HY, Chen XH (2016) Linguistic hesitant fuzzy multi-criteria decision-making method based on evidential reasoning. Int J Syst Sci 47(2):314–327

    Article  MathSciNet  MATH  Google Scholar 

  65. Liu AY, Liu FJ (2011) Research on method of analyzing the posterior weight of experts based on new evaluation scale of linguistic information. Chin J Manag Sci 19:149–155

    Google Scholar 

  66. Lu YJ, Zhang W (2003) Kernel function of index scale in AHP scale system. J Syst Eng 18(5):452–456

    Google Scholar 

  67. Bao GY, Lian XL, He M, Wang LL (2010) Improved 2-tuple linguistic representation model based on new linguistic evaluation scale. Control Decis 25(5):780–784

    MathSciNet  Google Scholar 

  68. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 42(2):263–292

    Article  MATH  Google Scholar 

  69. Wu LF, Liu SF, Yang YJ (2015) A model to determine OWA weights and its application in energy technology evaluation. Int J Intell Syst 30(7):798–806

    Article  Google Scholar 

  70. Liu XW (2012) Models to determine parameterized ordered weighted averaging operators using optimization criteria. Inf Sci 190:27–55

    Article  MathSciNet  MATH  Google Scholar 

  71. Xu ZS (2005) An overview of methods for determining OWA weights. Int J Intell Syst 20(8):843–865

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors thank the editors and anonymous reviewers for their helpful comments and suggestions. This work was supported by the National Natural Science Foundation of China (Nos. 71571193).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jian-qiang Wang.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Yx., Wang, Jq., Wang, J. et al. An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options. Neural Comput & Applic 28, 2745–2765 (2017). https://doi.org/10.1007/s00521-016-2203-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2203-1

Keywords

Navigation