Comparative Analysis of MCDM Methods for Assessing the Severity of Chronic Liver Disease

  • Andrzej PiegatEmail author
  • Wojciech Sałabun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9119)


The paper presents the Characteristic Objects method as a potential multi-criteria decision-making method for use in medical issues. The proposed approach is compared with TOPSIS and AHP. For this purpose, assessment of the severity of Chronic Liver Disease (CLD) is used. The simulation experiment is presented on the basis of the Model For End-Stage Liver Disease (MELD). The United Network for Organ Sharing (UNOS) and Eurotransplant use MELD for prioritizing allocation of liver transplants. MELD is calculated from creatinine, bilirubin and international normalized ratio of the prothrombin time (INR). The correctness of the selection is examined among randomly selected one million pairs of patients. The result is expressed as a percentage of agreement between the assessed method and MELD selection. The Characteristic Objects method is completely free of the rank reversal phenomenon, obtained by using the set of characteristic objects. In this approach, the assessment of each alternative is obtained on the basis of the distance from characteristic objects and their values. As a result, correctness of the selection obtained by using the Characteristic Objects method is higher than those obtained by TOPSIS or AHP techniques.




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  1. 1.
    Blair, A.R., Mandelker, G.N., Saaty, T.L., Whitaker, R.: Forecasting the resurgence of the u.s. economy in 2010: An expert judgment approach. Socio-Economic Planning Sciences 44(3), 114–121 (2010)CrossRefGoogle Scholar
  2. 2.
    Boran, F.E., Gen, S., Kurt, M., Akay, D.: A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications 36(8), 11363–11368 (2009)CrossRefGoogle Scholar
  3. 3.
    Cholongitas, E., Marelli, L., Shusang, V., Senzolo, M., Rolles, K., Patch, D., Burroughs, A.K.: A systematic review of the performance of the model for end-stage liver disease (MELD) in the setting of liver transplantation. Liver Transplantation 12(7), 1049–1061 (2006)CrossRefGoogle Scholar
  4. 4.
    Dolan, J.G., Isselhardt, B.J., Cappuccio, J.D.: The Analytic Hierarchy Process in Medical Decision Making: A Tutorial. Medical Decision Making 9(1), 40–50 (1989)CrossRefGoogle Scholar
  5. 5.
    Dong, Y., Zhang, G., Hong, W.C., Xu, Y.: Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems 49(3), 281–289 (2010)CrossRefGoogle Scholar
  6. 6.
    Figueira, J., Greco, S., Ehrgott, M.: Multiple Criteria Decision Analysis: State of the Art Surveys. Springer, New York (2004)Google Scholar
  7. 7.
    Garca-Cascalesa, M.S., Lamata, M.T.: On rank reversal and TOPSIS method. Mathematical and Computer Modelling 56(5-6), 10–19 (2012)Google Scholar
  8. 8.
    Hsu, P.F., Hsu, M.G.: Optimizing the information outsourcing practices of primary care medical organizations using entropy and TOPSIS. Quality and Quantity 42(2), 181–201 (2008)CrossRefGoogle Scholar
  9. 9.
    Hwang, C.L., Lai, Y.J., Liu, T.Y.: A new approach for multiple-objective decision-making. Computers and Operations Research 20(8), 889–899 (1993)CrossRefzbMATHGoogle Scholar
  10. 10.
    Hwang, C.L., Yoon, K.P.: Multiple attribute decision making: Methods and applications. Springer, New York (1981)CrossRefzbMATHGoogle Scholar
  11. 11.
    Ioannou, G.N., Perkins, J.D., Carithers Jr., R.L.: Liver transplantation for hepatocellular carcinoma: Impact of the MELD allocation system and predictors of survival. Gastroenterology 134(5), 1342–1351 (2008)CrossRefGoogle Scholar
  12. 12.
    Jung, G.E., Encke, J., Schmidt, J., Rahmel, A.: Model for end-stage liver disease. New basis of allocation for liver transplantations. Chirurg 79(2), 157–163 (2008)CrossRefGoogle Scholar
  13. 13.
    Kamath, P.S., Kim, W.: The model for end-stage liver disease (MELD). Hepatology 45(3), 797–805 (2007)CrossRefGoogle Scholar
  14. 14.
    Kamath, P.S., Wiesner, R.H., Malinchoc, M., Kremers, W., Therneau, T.M., Kosberg, C.L., D’Amico, G., Dickson, E.R., Kim, W.R.: A model to predict survival in patients with end-stage liver disease. Hepatology 33(2), 464–470 (2001)CrossRefGoogle Scholar
  15. 15.
    Karami, E.: Appropriateness of farmers adoption of irrigation methods: The application of the AHP model. Agricultural Systems 87(1), 101–119 (2006)CrossRefGoogle Scholar
  16. 16.
    Kim, Y., Chung, E.S., Jun, S.M., Kim, S.U.: Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS. Resources Conservation and Recycling 73, 23–32 (2013)CrossRefGoogle Scholar
  17. 17.
    Kuo, R.J., Wu, Y.H., Hsu, T.S.: Integration of fuzzy set theory and TOPSIS into HFMEA to improve outpatient service for elderly patients in Taiwan. Journal of the Chinese Medical Association 75(7), 341–348 (2012)CrossRefGoogle Scholar
  18. 18.
    La Scalia, G., et al.: Multi-criteria decision making support system for pancreatic islet transplantation. Expert Systems with Applications 38(4), 3091–3097 (2011)CrossRefzbMATHGoogle Scholar
  19. 19.
    Lai, Y.J., Liu, T.Y., Hwang, C.L.: TOPSIS for MODM. European Journal of Operational 76(3), 486–500 (1994)CrossRefzbMATHGoogle Scholar
  20. 20.
    Liberatore, M.J., Nydick, R.L.: The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research 189(1), 194–207 (2008)CrossRefzbMATHGoogle Scholar
  21. 21.
    Lin, C.T., Tsai, M.C.: Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS. Quality and Quantity 44(2), 375–390 (2010)CrossRefGoogle Scholar
  22. 22.
    Malinchoc, M., Kamath, P.S., Gordon, F.D., Peine, C.J., Rank, J., ter Borg, P.C.: A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology 31(4), 864–871 (2000)CrossRefGoogle Scholar
  23. 23.
    Milani, A.S., Shanian, A., Madoliat, R., Nemes, J.A.: The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection. Structural and Multidisciplinary Optimization 29(4), 312–318 (2005)CrossRefGoogle Scholar
  24. 24.
    Padilla-Garrido, N., et al.: Multicriteria Decision Making in Health Care Using the Analytic Hierarchy Process and Microsoft Excel. Medical Decision Making (first published on May 14, 2014)Google Scholar
  25. 25.
    Piegat, A.: Fuzzy Modeling and Control. Springer, New York (2001)CrossRefzbMATHGoogle Scholar
  26. 26.
    Piegat, A., Sałabun, W.: Nonlinearity of human multi-criteria in decision-making. Journal of Theoretical and Applied Computer Science 6(3), 36–49 (2012)Google Scholar
  27. 27.
    Piegat, A., Sałabun, W.: Identification of a Multicriteria Decision-Making Model Using the Characteristic Objects Method. Applied Computational Intelligence and Soft Computing (2014)Google Scholar
  28. 28.
    Sałabun, W.: The use of fuzzy logic to evaluate the nonlinearity of human multi-criteria used in decision making. Przeglad Elektrotechniczny (Electrical Review) 88(10b), 235–238 (2012)Google Scholar
  29. 29.
    Sałabun, W.: The mean error estimation of TOPSIS method using a fuzzy reference models. Journal of Theoretical and Applied Computer Science 7(3), 40–50 (2013)Google Scholar
  30. 30.
    Sałabun, W.: Application of the Fuzzy Multi-criteria Decision-Making Method to Identify Nonlinear Decision Models. International Journal of Computer Applications 89(15), 1–6 (2014)CrossRefGoogle Scholar
  31. 31.
    Sałabun, W.: Reduction in the number of comparisons required to create matrix of expert judgment in the COMET method. Management and Production Engineering Review 5(3), 62–69 (2014)Google Scholar
  32. 32.
    Sałabun, W.: The Characteristic Objects Method: A New Distance-based Approach to Multicriteria Decision-making Problems. Journal of Multi-Criteria Decision Analysis 21(3-4) (first published on July 4, 2014)Google Scholar
  33. 33.
    Saaty, T.L.: Decision making the analytic hierarchy and network processes (AHP/ANP). Journal of Systems Science and Systems Engineering 13(1), 1–35 (2004)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Saaty, T.L.: Time dependent decision-making; dynamic priorities in the AHP/ANP: Generalizing from points to functions and from real to complex variables. Mathematical and Computer Modelling 46(78), 860–891 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Saaty, T.L.: Decision making the analytic hierarchy and network processes (AHP/ANP). International Journal Services Sciences 1(1), 83–98 (2008)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Saaty, T.L., Brandy, C.: The encyclicon, volume 2: a dictionary of complex decisions using the analytic network process. RWS Publications, Pittsburgh (2009)Google Scholar
  37. 37.
    Saaty, T.L., Shang, J.S.: An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane acts. European Journal of Operational Research 214(3), 703–715 (2011)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Saaty, T.L., Tran, L.T.: On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process. Mathematical and Computer Modelling 46(78), 962–975 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Shih, H.S., Shyur, H.J., Lee, E.S.: An extension of TOPSIS for group decision making. Mathematical and Computer Modelling 45(7-8), 801–813 (2007)CrossRefzbMATHGoogle Scholar
  40. 40.
    Sipahi, S., Timor, M.: The analytic hierarchy process and analytic network process: an overview of applications. Management Decision 48(5), 775–808 (2010)CrossRefGoogle Scholar
  41. 41.
    Soltanifar, M., Shahghobadi, S.: Survey on rank preservation and rank reversal in data envelopment analysis. Knowledge-Based Systems 60, 10–19 (2014)CrossRefGoogle Scholar
  42. 42.
    Sun, Y.F., Liang, Z.S., Shan, C.J., Viernstein, H., Unger, F.: Comprehensive evaluation of natural antioxidants and antioxidant potentials in Ziziphus jujuba Mill. var. spinosa (Bunge) Huex H. F. Chou fruits based on geographical origin by TOPSIS method. Food Chemistry 124(4), 1612–1619 (2011)CrossRefGoogle Scholar
  43. 43.
    Taleizadeh, A.A., Akhavan Niaki, S.T., Aryanezhad, M.B.: A hybrid method of Pareto, TOPSIS and genetic algorithm to optimize multi-product multiconstraint inventory control systems with random fuzzy replenishments. Mathematical and Computer Modeling 49(5-6), 1044–1057 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    Wang, Y.M., Luoc, Y.: On rank reversal in decision analysis. Mathematical and Computer Modelling 49(5-6), 1221–1229 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Wiesner, R.H., McDiarmid, S.V., Kamath, P.S., Edwards, E.B., Malinchoc, M., Kremers, W.K., Krom, R.A., Kim, W.R.: MELD and PELD: application of survival models to liver allocation. Liver Transplantation 7(7), 567–580Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Artificial Intelligence Methods and Applied Mathematics, Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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