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The Fruit Fly Optimization Algorithms for Patient-Centered Care Based on Interval Trapezoidal Type-2 Fuzzy Numbers

  • Junhua HuEmail author
  • Panpan Chen
  • Yan Yang
  • Yongmei Liu
  • Xiaohong Chen
Article
  • 16 Downloads

Abstract

Patient-centered care is an important part of the integrative medicine that places the patient at the center of the delivery of treatment, improves the efficiency of care and responds the patient’s needs and preferences. However, the weighting approach for patient-centered group decision making has merely studied the consistency of decision makers (DMs) and group decision. In this study, we aim to develop the fruit fly optimization algorithm (FOA) under the interval trapezoidal type-2 fuzzy numbers (ITrT2FNs). By lowering the deviation distance between each DM’s decision matrix and the group matrix, the optimal weight of DMs can be obtained. Then a novel patient-centered group decision-making model based on the ITrT2FNs and the multi-attributive border approximation area comparison (MABAC) method is proposed. Moreover, the entropy weight method is developed to determine the criteria weights. Finally, the new model is applied to address the realistic breast cancer treatment selection problem, and a comparative analysis is implemented to verify the flexibility and rationality of the extended FOA.

Keywords

Patient-centered group decision making Weights of decision makers Fruit fly optimization algorithm Extended MABAC Interval trapezoidal type-2 fuzzy numbers 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Numbers 71871229, 71771219).

References

  1. 1.
    Coulter, A.: The autonomous patient: ending paternalism in medical care. Lond. Nuffield Trust 3(6), 37–55 (2002)Google Scholar
  2. 2.
    Pelzang, R.: Time to learn: understanding patient-centred care. Br. J. Nurs. 19(14), 912 (2010)Google Scholar
  3. 3.
    Redman, R.W.: Patient-centered care: an unattainable ideal? Res. Theory Nurs. Pract. 18(1), 11 (2004)Google Scholar
  4. 4.
    Mccormack, B., Mccance, T.: Development of a framework for person-centred nursing. J. Adv. Nurs. 56(5), 472–479 (2006)Google Scholar
  5. 5.
    Steiger, N.J., Balog, A.: Realizing patient-centered care: putting patients in the center, not the middle. Front. Health Serv. Manag. 26(4), 15 (2010)Google Scholar
  6. 6.
    Chen, T.Y.: Collaborative decision-making method for patient-centered care based on interval type-2 fuzzy sets. J. Chin. Inst. Ind. Eng. 29(7), 494–513 (2012)Google Scholar
  7. 7.
    Lee, Y.Y., Lin, J.L.: Do patient autonomy preferences matter? Linking patient-centered care to patient-physician relationships and health outcomes. Soc. Sci. Med. 71(10), 1811–1818 (2010)Google Scholar
  8. 8.
    Hu, J., Pan, L., Yang, Y., Chen, H.: A group medical diagnosis model based on intuitionistic fuzzy soft sets. Appl. Soft Comput. 77, 453–466 (2019)Google Scholar
  9. 9.
    Yang, Y., Hu, J., Sun, R., Chen, X.: Medical tourism estinations prioritization using group decision making method with neutrosophic fuzzy preference relations. Sci. Iran. Trans. E Ind. Eng. 25(6), 3744–3764 (2018)Google Scholar
  10. 10.
    Yang, Y., Hu, J., Liu, Y., Chen, X.: Doctor recommendation based on an intuitionistic normal cloud model considering patient preferences. Cognit. Comput. (2018).  https://doi.org/10.1007/s12559-018-9616-3 Google Scholar
  11. 11.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—II. Inf. Sci. 8(4), 301–357 (1975)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A linguistic decision process in group decision making. Group Decis. Negot. 5(2), 165–176 (1996)zbMATHGoogle Scholar
  13. 13.
    Pang, J., Liang, J.: Evaluation of the results of multi-attribute group decision-making with linguistic information. Omega 40(3), 294–301 (2012)Google Scholar
  14. 14.
    Liu, P., Chen, S.M.: Multiattribute group decision making based on intuitionistic 2-tuple linguistic information. Inf. Sci. 430–431, 599–619 (2018)MathSciNetGoogle Scholar
  15. 15.
    Mendel, J.M.: A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul. Comput. 1(1), 59–69 (2016)Google Scholar
  16. 16.
    Zhang, X.Y., Zhang, H.Y., Wang, J.Q.: Discussing incomplete 2-tuple fuzzy linguistic preference relations in multi-granular linguistic MCGDM with unknown weight information. Soft. Comput. 20(3), 958–969 (2017).  https://doi.org/10.1007/s00500-017-2915-x Google Scholar
  17. 17.
    Pei, Z., Zheng, L.: New unbalanced linguistic scale sets: the linguistic information representations and applications. Comput. Ind. Eng. 105, 377–390 (2017)Google Scholar
  18. 18.
    Zhang, Z., Zhou, Q., Wu, C., Li, H.: Dissipativity-based reliable interval type-2 fuzzy filter design for uncertain nonlinear systems. Int. J. Fuzzy Syst. 20(2), 390–402 (2018)MathSciNetGoogle Scholar
  19. 19.
    Tellez-Velazquez, A., Molina-Lozano, H., Villa-Vargas, L.A., Cruz-Barbosa, R., Lugo-Gonzalez, E., Batyrshin, I.Z., Rudas, I.J.: A feasible genetic optimization strategy for parametric interval type-2 fuzzy logic systems. Int. J. Fuzzy Syst. 20(1), 318–338 (2018)Google Scholar
  20. 20.
    Hu, J., Chen, P., Yang, Y.: An interval type-2 fuzzy similarity-based MABAC approach for patient-centered care. Mathematics 7(2), 140 (2019).  https://doi.org/10.3390/math7020140 Google Scholar
  21. 21.
    Chen, S.M., Yang, M.W., Lee, L.W., Yang, S.W.: Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets. Expert Syst. Appl. Int. J. 44(12), 1665–1673 (2017)Google Scholar
  22. 22.
    Mendel, J.M.: Computing with words and its relationships with fuzzistics. Inf. Sci. 177(4), 988–1006 (2007)MathSciNetGoogle Scholar
  23. 23.
    Mendel, J.M., Wu, H.: Type-2 fuzzistics for symmetric interval type-2 fuzzy sets: part 1, forward problems. IEEE Trans. Fuzzy Syst. 14(6), 781–792 (2006)Google Scholar
  24. 24.
    Mendel, J.M., John, R.I., Liu, F.: Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14(6), 808–821 (2006)Google Scholar
  25. 25.
    Dijkman, J.G., Haeringen, H.V., Lange, S.J.D.: Fuzzy numbers. J. Math. Anal. Appl. 92(2), 301–341 (1983)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Greenfield, S., Chiclana, F., Coupland, S., John, R.: The collapsing method of defuzzification for discretised interval type-2 fuzzy sets. Inf. Sci. 179(13), 2055–2069 (2007)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Sinha, B., Das, A., Bera, U.K.: Profit maximization solid transportation problem with trapezoidal interval type-2 fuzzy numbers. Int. J. Appl. Comput. Math. 2(1), 41–56 (2015)MathSciNetGoogle Scholar
  28. 28.
    Abdullah, L., Zulkifli, N.: Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: an application to human resource management. Expert Syst. Appl. 42(9), 4397–4409 (2015)Google Scholar
  29. 29.
    Wu, D., Mendel, J.M.: A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets. Inf. Sci. 179(8), 1169–1192 (2009)MathSciNetGoogle Scholar
  30. 30.
    Whelan, M.E., Goode, A.D., Hickman, I.J., Eakin, E.G., Reeves, M.M.: Telephone-delivered weight management services in the hospital outpatient setting: decision-makers’ perceptions of their use in routine practice. Nutr. Diet. 74(3), 261–267 (2017)Google Scholar
  31. 31.
    Yue, Z.: An extended TOPSIS for determining weights of decision makers with interval numbers. Knowl. Based Syst. 24(1), 146–153 (2011)Google Scholar
  32. 32.
    Honert, R.C.V.D.: Decisional power in group decision making: a note on the allocation of group members’ weights in the multiplicative AHP and SMART. Group Decis. Negot. 10(3), 275–286 (2001)Google Scholar
  33. 33.
    Onar, S.C., Oztaysi, B., Kahraman, C.: Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: a case study. Int. J. Comput. Intell. Syst. 7(5), 1002–1021 (2014)Google Scholar
  34. 34.
    Zhou, Y.N., Zhu, Y.A.: Algorithm for adjusting weights of decision-makers in multi-attribute group decision-making based on grey system theory. Control Decis. 27(7), 1113–1116 (2012)MathSciNetGoogle Scholar
  35. 35.
    Liu, H.C., You, J.X., Duan, C.Y.: An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment. Int. J. Prod. Econ. (2017).  https://doi.org/10.1016/j.ijpe.2017.03.008 Google Scholar
  36. 36.
    Yue, Z.: Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information. Expert Syst. Appl. 39(7), 6343–6350 (2012)Google Scholar
  37. 37.
    Liu, W., Li, L.: An approach to determining the integrated weights of decision makers based on interval number group decision matrices. Knowl. Based Syst. 90(C), 92–98 (2015)Google Scholar
  38. 38.
    Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl. Based Syst. 26(2), 69–74 (2012)Google Scholar
  39. 39.
    Xu, W., Deng, X., Li, J.: A new fuzzy portfolio model based on background risk using MCFOA. Int. J. Fuzzy Syst. 17(2), 246–255 (2015)MathSciNetGoogle Scholar
  40. 40.
    Mousavi, S.M., Alikar, N., Niaki, S.T.A.: An improved fruit fly optimization algorithm to solve the homogeneous fuzzy series–parallel redundancy allocation problem under discount strategies. Soft. Comput. 20(6), 2281–2307 (2016)Google Scholar
  41. 41.
    Hu, R., Wen, S., Zeng, Z., Huang, T.: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221(C), 24–31 (2017)Google Scholar
  42. 42.
    Wang, Y.J., Lee, H.S.: Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Comput. Math Appl. 53(11), 1762–1772 (2007)MathSciNetzbMATHGoogle Scholar
  43. 43.
    Aksoy, S., Ozbuk, M.Y.: Multiple criteria decision making in hotel location: does it relate to postpurchase consumer evaluations? Tour. Manag. Perspect. 22, 73–81 (2017)Google Scholar
  44. 44.
    Wang, J., Wang, J.Q., Tian, Z.P., Zhao, D.Y.: A multihesitant fuzzy linguistic multicriteria decision-making approach for logistics outsourcing with incomplete weight information. Int. Trans. Oper. Res. 25(3), 831–856 (2017)MathSciNetzbMATHGoogle Scholar
  45. 45.
    Yang, Y., Hu, J., Liu, Y., Chen, X.: A multi-period hybrid decision support model for medical diagnosis and treatment based on similarities and three-way decision theory. Expert Syst. (2019).  https://doi.org/10.1111/exsy.12377 Google Scholar
  46. 46.
    Chen, T.: 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 (2013)MathSciNetzbMATHGoogle Scholar
  47. 47.
    Pamucar, D., Cirovic, G.: The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison (MABAC). Expert Syst. Appl. 42(6), 3016–3028 (2015)Google Scholar
  48. 48.
    Hu, J., Zhang, X., Yang, Y., Liu, Y., Chen, X.: New doctors ranking system based on VIKOR method. Int. Trans. Oper. Res. (2018).  https://doi.org/10.1111/itor.12569 Google Scholar
  49. 49.
    Liang, P., Hu, J., Liu, Y., Chen, X.: Public resources allocation using an uncertain cooperative game among vulnerable groups. Kybernetes (2018).  https://doi.org/10.1108/k-03-2018-0146 Google Scholar
  50. 50.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)zbMATHGoogle Scholar
  51. 51.
    Chen, T.: A linear assignment method for multiple-criteria decision analysis with interval type-2 fuzzy sets. Appl. Soft Comput. J. 13(5), 2735–2748 (2013)Google Scholar
  52. 52.
    Chen, T.: An ELECTRE-based outranking method for multiple criteria group decision making using interval type-2 fuzzy sets. Inf. Sci. 263(3), 1–21 (2014)MathSciNetzbMATHGoogle Scholar
  53. 53.
    Ma, X., Wu, P., Zhou, L., Chen, H., Zheng, T., Ge, J.: Approaches based on interval type-2 fuzzy aggregation operators for multiple attribute group decision making. Int. J. Fuzzy Syst. 18(4), 697–715 (2015)MathSciNetGoogle Scholar
  54. 54.
    Chen, S., Chen, S.: A new method for handling multicriteria fuzzy decision-making problems using FN-IOWA operators. Cybern. Syst. 34(2), 109–137 (2003)zbMATHGoogle Scholar
  55. 55.
    Abdullah, L., Otheman, A.: A new entropy weight for sub-criteria in interval type-2 fuzzy TOPSIS and its application. Int. J. Intell. Syst. Appl. 5(2), 25–33 (2013)Google Scholar
  56. 56.
    Wei, G.W.: Extension of TOPSIS method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. Knowl. Inf. Syst. 25, 623–634 (2010)Google Scholar

Copyright information

© Taiwan Fuzzy Systems Association 2019

Authors and Affiliations

  • Junhua Hu
    • 1
    Email author
  • Panpan Chen
    • 1
  • Yan Yang
    • 1
  • Yongmei Liu
    • 1
  • Xiaohong Chen
    • 1
  1. 1.School of BusinessCentral South UniversityChangshaChina

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