Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome

Abstract

Multi-criteria decision-making (MCDM) methods are commonly used in many fields of research, e.g., engineering and manufacturing systems, water resources studies , medicine, and etc. However, there is no effective approach of selecting a MCDM method to problem, which is solved. The formal requirements of each MCDM method are not sufficient because most methods would seem to be appropriate for most problems. Therefore, the main purpose of the paper is a comparison of accuracy selected MCDM methods. Proposed approach is presented on the example of mortality in patients with acute coronary syndrome. Additionally, the paper presents characteristic objects method (COMET) as a potential decision making method for use in medical problems, which accuracy is compared with TOPSIS and AHP. In the experimental study, the average and standard deviation of the root mean square error of evaluations are examined for groups of randomly selected patients, each described by age, blood pressure, and heart rate. Then, the correctness of choosing the patient in the best and worst condition is also examined among randomly selected pairs. As a result of the experimental study, rankings obtained by the COMET method are distinctly more accurate than those obtained by TOPSIS or AHP techniques. The COMET method, in the opposite of others method, is completely free of the rank reversal phenomenon, which is identified as a main source of problems with evaluations accuracy.

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References

  1. Blair AR, Mandelker GN, Saaty TL, Whitaker R (2010) Forecasting the resurgence of the U.S. economy in 2010: an expert judgment approach. Soc-Econ Plan Sci 44(3):114–121

    Article  Google Scholar 

  2. Bradshaw PJ, Ko DT, Newman AM, Donovan LR, Tu JV (2007) Validation of the thrombolysis in myocardial infarction (TIMI) risk index for predicting early mortality in a population-based cohort of STEMI and non-STEMI patients. Can J Cardiol 23(1):51–56

    Article  Google Scholar 

  3. Dolan JG, Isselhardt BJ, Cappuccio JD (1989) The analytic hierarchy process in medical decision making: a tutorial. Med Decis Mak 9(1):40–50

    Article  Google Scholar 

  4. Dong Y, Zhang G, Hong WC, Xu Y (2010) Consensus models for AHP group decision making under row geometric mean prioritization method. Decis Support Syst 49(3):281–289

    Article  Google Scholar 

  5. Figueira J, Greco S, Ehrgott M (2004) Multiple criteria decision analysis: state of the art surveys. Springer, New York

    Google Scholar 

  6. Garca-Cascalesa MS, Lamata MT (2012) On rank reversal and TOPSIS method. Math Comput Model 56(5–6):10–19

    MathSciNet  Google Scholar 

  7. Herrera-Viedma E, Cabrerizo FJ, Kacprzyk J, Pedrycz W (2014) A review of soft consensus models in a fuzzy environment Inf Fusion. 17:4–13

  8. Hwang CL, Lai YJ, Liu TY (1993) A new approach for multiple-objective decision-making. Comput Oper Res 20(8):889–899

    Article  MATH  Google Scholar 

  9. Hwang CL, Yoon KP (1981) Multiple attribute decision making: methods and applications. Springer, New York

    Google Scholar 

  10. Karami E (2006) Appropriateness of farmers adoption of irrigation methods: the application of the AHP model. Agric Syst 87(1):101–119

    Article  Google Scholar 

  11. Kaufmann A, Gupta M (1988) Fuzzy mathematical models in engineering and management science. Elsevier Science Publishers, Amsterdam

    Google Scholar 

  12. Kim Y, Chung ES, Jun SM, Kim SU (2013) Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS. Resour Conserv Recycl 73:23–32

    Article  Google Scholar 

  13. Kumar A, Singh P, Kaur A (2010) RM approach for ranking of generalized trapezoidal fuzzy numbers. Fuzzy Inf Eng 2(1):37–47

    Article  MATH  Google Scholar 

  14. Kuo RJ, Wu YH, Hsu TS (2012) Integration of fuzzy set theory and TOPSIS into HFMEA to improve outpatient service for elderly patients in Taiwan. J Chin Med Assoc 75(7):341–348

    Article  Google Scholar 

  15. La Scalia G, Aiello G, Rastellini C, Micale R, Cicalese L (2011) Multi-criteria decision making support system for pancreatic islet transplantation. Expert Syst Appl 38(4):3091–3097

    Article  Google Scholar 

  16. Lai YJ, Liu TY, Hwang CL (1994) TOPSIS for MODM. Eur J Oper 76(3):486–500

    Article  MATH  Google Scholar 

  17. 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 

  18. Milani AS, Shanian A, Madoliat R, Nemes JA (2005) The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection. Struct Multidiscip Optim 29(4):312–318

    Article  Google Scholar 

  19. Morrow DA, Antman EM, Giugliano RP, Cairns R, Charlesworth A, Murphy SA, de Lemos JA, McCabe CH, Braunwald E (2001) A simple risk index for rapid initial triage of patients with ST-elevation myocardial infarction: an InTIME II substudy. Lancet 358(9293):1571–1575

    Article  Google Scholar 

  20. Padilla-Garrido N, Aguado-Correa F, Cortijo-Gallego V, Lpez-Camacho F (2014) Multicriteria decision making in health care using the analytic hierarchy process and microsoft excel. Medical Decision Making, first published on May 14th

  21. Pedrycz W, Ekel P, Parreiras R (2011) Fuzzy multicriteria decision making: models, methods and applications. Wiley, Chichester

    Google Scholar 

  22. Piegat A (2001) Fuzzy modeling and control. Springer, New York

    Google Scholar 

  23. Piegat A, Sałabun W (2012) Nonlinearity of human multi-criteria in decision-making. J Theor Appl Comput Sci 6(3):36–49

    Google Scholar 

  24. Ross TJ (2010) Fuzzy logic with engineering applications. Wiley, Chichester

    Google Scholar 

  25. Sałabun W (2012) The use of fuzzy logic to evaluate the nonlinearity of human multi-criteria used in decision making. Przeglad Elektrotechniczny (Electr Rev) 88(10b):235–238

    Google Scholar 

  26. Sałabun W (2013) The mean error estimation of TOPSIS method using a fuzzy reference models. J Theor Appl Comput Sci 7(3):40–50

    Google Scholar 

  27. Sałabun W (2014) Application of the fuzzy multi-criteria decision-making method to identify nonlinear decision models. Int J Comput Appl 89(15):1–6

    Google Scholar 

  28. Sałabun W (2015) The characteristic objects method: a new distance-based approach to multicriteria decision-making problems. J Multi-Criteria Decis Anal 22(1–2)

  29. Saaty TL (2004) Decision making the analytic hierarchy and network processes (AHP/ANP). J Syst Sci Syst Eng 13(1):1–35

    Article  MathSciNet  Google Scholar 

  30. Saaty TL (2007) Time dependent decision-making; dynamic priorities in the AHP/ANP: generalizing from points to functions and from real to complex variables. Math Comput Model 46(78):860–891

    Article  MATH  MathSciNet  Google Scholar 

  31. Saaty TL (2008) Decision making the analytic hierarchy and network processes (AHP/ANP). Int J Serv Sci 1(1):83–98

    MathSciNet  Google Scholar 

  32. Saaty TL, Brandy C (2009) The encyclicon, volume 2: a dictionary of complex decisions using the analytic network process. RWS Publications, Pittsburgh

    Google Scholar 

  33. Saaty TL, Shang JS (2011) An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: prioritizing divergent intangible humane acts. Eur J Oper Res 214(3):703–715

    Article  Google Scholar 

  34. Saaty TL, Tran LT (2007) On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process. Math Comput Model 46(78):962–975

    Article  MATH  MathSciNet  Google Scholar 

  35. Shih HS, Shyur HJ, Lee ES (2007) An extension of TOPSIS for group decision making. Math Comput Model 45(7–8):801–813

    Article  MATH  Google Scholar 

  36. Sipahi S, Timor M (2010) The analytic hierarchy process and analytic network process: an overview of applications. Manag Decis 48(5):775–808

    Article  Google Scholar 

  37. Soltanifar M, Shahghobadi S (2014) Survey on rank preservation and rank reversal in data envelopment analysis. Knowl Based Syst 60:10–19

    Article  Google Scholar 

  38. Sun YF, Liang ZS, Shan CJ, Viernstein H, Unger F (2011) 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 Chem 124(4):1612–1619

    Article  Google Scholar 

  39. Taleizadeh AA, Akhavan Niaki ST, Aryanezhad MB (2009) A hybrid method of Pareto, TOPSIS and genetic algorithm to optimize multi-product multiconstraint inventory control systems with random fuzzy replenishments. Math Comput Model 49(5–6):1044–1057

    Article  MATH  Google Scholar 

  40. Wang G, Wang H (2001) Non-fuzzy versions of fuzzy reasoning in classical logics. Inf Sci 138(1–4):211–236

    Article  MATH  MathSciNet  Google Scholar 

  41. Wang YM, Luoc Y (2009) On rank reversal in decision analysis. Math Comput Model 49(5–6):1221–1229

    Article  MATH  MathSciNet  Google Scholar 

  42. Wiviott SD, Morrow DA, Frederick PD, Antman EM, Braunwald E (2006) Application of the Thrombolysis in Myocardial Infarction risk index in non-ST-segment elevation myocardial infarction: evaluation of patients in the National Registry of Myocardial Infarction. J Am Coll Cardiol 47(8):1553–1558

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  44. Zimmermann HJ (2001) Fuzzy set theory and its applications. Kluwer, Boston

    Google Scholar 

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Correspondence to Wojciech Sałabun.

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Sałabun, W., Piegat, A. Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome. Artif Intell Rev 48, 557–571 (2017). https://doi.org/10.1007/s10462-016-9511-9

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Keywords

  • Multi-criteria decision-making
  • COMET method
  • AHP
  • TOPSIS
  • Characteristic objects
  • Fuzzy set theory