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G. Scalia, G. Aiello, C. Rastellini, R. Micale, L. Cicalese (2011)
Multi-Criteria Decision Making support system for pancreatic islet transplantationExpert Syst. Appl., 38
Yingming Wang, Ying Luo (2009)
On rank reversal in decision analysisMath. Comput. Model., 49
C. Hwang, K. Yoon (1981)
Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey, 186
T. Saaty (2004)
Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP)Journal of Systems Science and Systems Engineering, 13
Amit Kumar, Pushpinder Singh, A. Kaur, Parmpreet Kaur (2010)
RM approach for ranking of generalized trapezoidal fuzzy numbersFuzzy Information and Engineering, 2
A Piegat, W Sałabun (2012)
Nonlinearity of human multi-criteria in decision-makingJ Theor Appl Comput Sci, 6
T. Saaty (2008)
DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESSInternational Journal of Services Sciences, 1
D. Morrow, E. Antman, R. Giugliano, Richard Cairns, A. Charlesworth, S. Murphy, J. Lemos, C. Mccabe, E. Braunwald (2001)
A simple risk index for rapid initial triage of patients with ST-elevation myocardial infarction: an InTIME II substudyThe Lancet, 358
Andrew Blair, Robert Nachtmann, T. Saaty, R. Whitaker (2002)
Forecasting the resurgence of the US economy in 2001: an expert judgment approachSocio-economic Planning Sciences, 36
TL Saaty, C Brandy (2009)
The encyclicon, volume 2: a dictionary of complex decisions using the analytic network process
T. Saaty, L. Tran (2007)
On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy ProcessMath. Comput. Model., 46
S. Wiviott, D. Morrow, P. Frederick, E. Antman, E. Braunwald (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.Journal of the American College of Cardiology, 47 8
M. Liberatore, R. Nydick (2008)
The analytic hierarchy process in medical and health care decision making: A literature reviewEur. J. Oper. Res., 189
E. Karami (2006)
Appropriateness of farmers’ adoption of irrigation methods: The application of the AHP modelAgricultural Systems, 87
H. Shih, Huan-Jyh Shyur, E. Lee (2007)
An extension of TOPSIS for group decision makingMath. Comput. Model., 45
Yucheng Dong, Guiqing Zhang, Wei‐Chiang Hong, Yinfeng Xu (2010)
Consensus models for AHP group decision making under row geometric mean prioritization methodDecis. Support Syst., 49
M. García-Cascales, M. Lamata (2012)
On rank reversal and TOPSIS methodMath. Comput. Model., 56
A. Piegat (2001)
Fuzzy Modeling and Control
L. Yang (1999)
Fuzzy Logic with Engineering Applications
T. Ross (2010)
Fuzzy Logic with Engineering Applications: Ross/Fuzzy Logic with Engineering Applications
Yan-fang Sun, Zongsuo Liang, C. Shan, H. Viernstein, F. Unger (2011)
Comprehensive evaluation of natural antioxidants and antioxidant potentials in Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chou fruits based on geographical origin by TOPSIS methodFood Chemistry, 124
A. Kaufmann, M. Gupta (1988)
Fuzzy mathematical models in engineering and management science
S. Sipahi, Mehpare Timor (2010)
The analytic hierarchy process and analytic network process: an overview of applicationsManagement Decision, 48
TL Saaty (2008)
Decision making the analytic hierarchy and network processes (AHP/ANP)Int J Serv Sci, 1
W. Sałabun (2015)
The Characteristic Objects Method: A New Distance‐based Approach to Multicriteria Decision‐making ProblemsJournal of Multi-criteria Decision Analysis, 22
Andrew Blair, G. Mandelker, T. Saaty, R. Whitaker (2015)
Forecasting the Resurgence of the U.S. Economy in 2010: An Expert Judgment ApproachReview of Economics and Finance, 5
M. Côrrea-Giannella, Alexandre Amaral (2009)
Pancreatic islet transplantationDiabetology and Metabolic Syndrome, 1
W Sałabun (2013)
The mean error estimation of TOPSIS method using a fuzzy reference modelsJ Theor Appl Comput Sci, 7
R. Kuo, Yung-Hung Wu, Tsung-Shin Hsu (2012)
Integration of fuzzy set theory and TOPSIS into HFMEA to improve outpatient service for elderly patients in TaiwanJournal of the Chinese Medical Association, 75
Guojun Wang, Hao Wang (2001)
Non-fuzzy versions of fuzzy reasoning in classical logicsInf. Sci., 138
A. Piegat, W. Sałabun (2012)
Nonlinearity of human multi-criteria in decision-makingApplied Computer Science, 6
A. Taleizadeh, S. Niaki, M. Aryanezhad (2009)
A hybrid method of Pareto, TOPSIS and genetic algorithm to optimize multi-product multi-constraint inventory control systems with random fuzzy replenishmentsMath. Comput. Model., 49
C. Hwang, Young-Jou Lai, Ting-Yun Liu (1993)
A new approach for multiple objective decision makingComput. Oper. Res., 20
Robert Lin (2014)
NOTE ON FUZZY SETSYugoslav Journal of Operations Research, 24
Shu-Jen Chen, C. Hwang (1992)
Fuzzy Multiple Attribute Decision Making - Methods and Applications, 375
E. Herrera-Viedma, F. Cabrerizo, J. Kacprzyk, W. Pedrycz (2014)
A review of soft consensus models in a fuzzy environmentInf. Fusion, 17
M. Soltanifar, S. Shahghobadi (2014)
Survey on rank preservation and rank reversal in data envelopment analysisKnowl. Based Syst., 60
A. Milani, A. Shanian, R. Madoliat, J. Nemes (2005)
The effect of normalization norms in multiple attribute decision making models: a case study in gear material selectionStructural and Multidisciplinary Optimization, 29
T. Saaty, J. Shang (2011)
An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane actsEur. J. Oper. Res., 214
H. Zimmermann (1985)
Fuzzy Set Theory - and Its Applications
W. Sałabun (2013)
The mean error estimation of TOPSIS method using a fuzzy reference modelsApplied Computer Science, 7
P. Bradshaw, D. Ko, A. Newman, Linda Donovan, J. Tu (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.The Canadian journal of cardiology, 23 1
W. Sałabun (2014)
Application of the fuzzy multi-criteria decision-making method to identify nonlinear decision modelInternational Journal of Computer Applications, 89
JG Dolan, BJ Isselhardt, JD Cappuccio (1989)
The analytic hierarchy process in medical decision making: a tutorialMed Decis Mak, 9
LA Zadeh (1965)
Fuzzy setsInf Control, 8
T. Saaty (2007)
Time dependent decision-making; dynamic priorities in the AHP/ANP: Generalizing from points to functions and from real to complex variablesMath. Comput. Model., 46
Yeonjoo Kim, E. Chung, S. Jun, S. Kim (2013)
Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSISResources Conservation and Recycling, 73
W. Sałabun (2012)
The use of fuzzy logic to evaluate the nonlinearity of human multi-criteria used in decision makingPrzegląd Elektrotechniczny
Witold Pedrycz, P. Ekel, R. Parreiras (2010)
Fuzzy Multicriteria Decision-Making: Models, Methods and Applications
S. Greco, M. Ehrgott, J. Figueira (2005)
Multiple criteria decision analysis: state of the art surveysOperations Research and Management Science
N. Padilla-Garrido, F. Aguado-Correa, Virginia Cortijo-Gallego, Francisca López-Camacho (2014)
Multicriteria Decision Making in Health Care Using the Analytic Hierarchy Process and Microsoft ExcelMedical Decision Making, 34
Young-Jou Lai, Ting-Yun Liu, C. Hwang (1994)
TOPSIS for MODMEuropean Journal of Operational Research, 76
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.
Artificial Intelligence Review – Springer Journals
Published: Sep 3, 2016
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