Ortoper Bulanık Kümelerle Bir Karar Verme Yaklaşımı: Ortoper Bulanık TOPSIS Metodu

Tıbbi karar verme, son yıllarda mühendisler için önemli bir araştırma alanı haline gelmiştir. Doktorlar daha çok istatistiksel araçlar kullandıklarından, tıbbi kararlarda çok kriterli karar verme modeli kurmak oldukça zordur. Bu noktada bulanık karar destek sistemleri, doktorlara görüşlerini sözel terimlerle ifade etme fırsatı sunar. Ortoper bulanık setlerin kullanılması ise karar vericilerin yani doktorların, tereddütlerini ifade etmeleri konusunda bulanık setler arasında en yüksek esnekliği sağlar. Bu çalışmada akut atak şikayeti ile hastaneye başvuran KOAH hastalarının hastane yatış sürelerinin sıralanması için Ortoper Bulanık TOPSIS (OFTOPSIS) metodu önerilmiştir. Bu yöntem doktorların tereddütlerini sözel terimlerle ifade etmede en yüksek esnekliği sağlarken, hastaların durumunu önceden doktorlar tarafından belirlenmiş kriterlere göre değerlendirir. Çalışmanın literature katkısı ilk kez OFTOPSIS metodunu geliştirmek ve tıbbi bir problemde uygulamasını yaparak faydasını göstermektir. Gerçek sıralama ve metot tarafından bulunan sıralama, pozitif bir ilişki varlığını gözlemlemek için Spearman korelasyon katsayısı kullanılarak karşılaştırılmıştır.

A Decision-Making Approach with Q-Rung Orthopair Fuzzy Sets: Orthopair Fuzzy TOPSIS Method

Medical decision-making has recently become a crucial research area due to its outputs related to the continuity of human life. Since the physicians used to employ statistical tools for a number of years, constructing a multi-attribute decision framework is quite difficult. In general, fuzzy decision aid systems provide flexibility to the physicians enabling them to express their opinions using linguistic variables. Moreover, orthopair fuzzy numbers allows the decision makers to represent their hesitations while providing linguistic data in both uncertain and hesitant environment. This paper proposes orthopair fuzzy TOPSIS (OFTOPSIS) methodology in order to rank the length of hospital stay of chronic obstructive pulmonary disease patients, who admitted to a chest diseases hospital with an acute exacerbation. The proposed method provides maximum flexibility to the physicians for expressing their hesitations to the system modelers, while assessing patients’ status according to pre-determined attributes. The novelty of this paper is to develop OFTOPSIS methodology, and conduct a case study in medical area to demonstrate the robustness of the proposed decision-making framework. The actual ranking and the ranking determined by OFTOPSIS method are compared by Spearman rank correlation coefficient to conclude whether there is a positive relationship between the ranking results.

PDF

___

[1] L. Zadeh, "Fuzzy Sets," Information and Control, vol. 8, no. 3, pp. 338-353, 1965.

[2] K. T. Atanassov, "Intuitionistic Fuzzy-Sets," (in English), Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87-96, Aug 1986.

[3] R. R. Yager and A. M. Abbasov, "Pythagorean membership grades, complex numbers, and decision making," International Journal of Intelligent Systems, vol. 28, no. 5, pp. 436-452, 2013.

[4] K. Atanassov, P. Vassilev, and R. Tcvetkov, "Intuitionistic fuzzy sets, measures and integrals," Prof. M. Drinov” Academic Publishing House, Sofia, 2013.

[5] R. R. Yager, "Generalized orthopair fuzzy sets," IEEE Transactions on Fuzzy Systems, vol. 25, no. 5, pp. 1222-1230, 2017.

[6] R. R. Yager and N. Alajlan, "Approximate reasoning with generalized orthopair fuzzy sets," Information Fusion, vol. 38, pp. 65-73, Nov 2017.

[7] B. P. Joshi, A. Singh, P. K. Bhatt, and K. S. Vaisla, "Interval valued q-rung orthopair fuzzy sets and their properties," Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5225-5230, 2018.

[8] X. D. Peng, J. G. Dai, and H. Garg, "Exponential operation and aggregation operator for q-rung orthopair fuzzy set and their decision-making method with a new score function," International Journal of Intelligent Systems, vol. 33, no. 11, pp. 2255-2282, Nov 2018.

[9] R. R. Yager, N. Alajlan, and Y. Bazi, "Aspects of generalized orthopair fuzzy sets," International Journal of Intelligent Systems, vol. 33, no. 11, pp. 2154-2174, Nov 2018.

[10]A. Habib, M. Akram, and A. Farooq, "q-Rung Orthopair Fuzzy Competition Graphs with Application in the Soil Ecosystem," Mathematics, vol. 7, no. 1, Jan 2019, Art. no. 91.

[11]W. S. Du, "Correlation and correlation coefficient of generalized orthopair fuzzy sets," International Journal of Intelligent Systems, vol. 34, no. 4, pp. 564-583, Apr 2019.

[12]P. D. Liu and J. L. Liu, "Some q-Rung Orthopai Fuzzy Bonferroni Mean Operators and Their Application to Multi-Attribute Group Decision Making," International Journal of Intelligent Systems, vol. 33, no. 2, pp. 315-347, Feb 2018.

[13]G. W. Wei, H. Gao, and Y. Wei, "Some q-rung orthopair fuzzy Heronian mean operators in multiple attribute decision making," International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1426-1458, Jul 2018.

[14]P. D. Liu, S. M. Chen, P. Wang, and Ieee, The q-Rung Orthopair Fuzzy Power Maclaurin Symmetric Mean Operators (Proceedings of 2018 Tenth International Conference on Advanced Computational Intelligence). 2018, pp. 156-161.

[15]K. Y. Bai, X. M. Zhu, J. Wang, and R. T. Zhang, "Some Partitioned Maclaurin Symmetric Mean Based on q-Rung Orthopair Fuzzy Information for Dealing with MultiAttribute Group Decision Making," Symmetry-Basel, vol. 10, no. 9, Sep 2018, Art. no. 383.

[16]P. D. Liu and P. Wang, "Some q-Rung Orthopair Fuzzy Aggregation Operators and their Applications to MultipleAttribute Decision Making," International Journal of Intelligent Systems, vol. 33, no. 2, pp. 259-280, Feb 2018.

[17]Y. H. Huang and G. W. Wei, "TODIM method for interval-valued Pythagorean fuzzy multiple attribute decision making," International Journal of KnowledgeBased and Intelligent Engineering Systems, vol. 22, no. 4, pp. 249-259, 2018.

[18]W. S. Du, "Minkowski-type distance measures for generalized orthopair fuzzy sets," International Journal of Intelligent Systems, vol. 33, no. 4, pp. 802-817, Apr 2018.

[19]L. Li, R. T. Zhang, J. Wang, X. P. Shang, and K. Y. Bai, "A Novel Approach to Multi-Attribute Group DecisionMaking with q-Rung Picture Linguistic Information," Symmetry-Basel, vol. 10, no. 5, May 2018, Art. no. 172.

[20]J. Wang, H. Gao, G. W. Wei, and Y. Wei, "Methods for Multiple-Attribute Group Decision Making with q-Rung Interval-Valued Orthopair Fuzzy Information and Their Applications to the Selection of Green Suppliers," Symmetry-Basel, vol. 11, no. 1, Jan 2019, Art. no. 56.

[21]G. W. Wei, C. Wei, J. Wang, H. Gao, and Y. Wei, "Some q-rung orthopair fuzzy maclaurin symmetric mean operators and their applications to potential evaluation of emerging technology commercialization," International Journal of Intelligent Systems, vol. 34, no. 1, pp. 50-81, Jan 2019.

[22]W. Yang and Y. F. Pang, "New q-rung orthopair fuzzy partitioned Bonferroni mean operators and their application in multiple attribute decision making," International Journal of Intelligent Systems, vol. 34, no. 3, pp. 439-476, Mar 2019.

[23]C. L. Hwang and K. Yoon, Multiple attribute decision making : methods and applications : a state-of-the-art survey (Lecture Notes in Economics and Mathematical Systems, no. 186). Berlin ; New York: Springer-Verlag, 1981, pp. x, 259 p.

[24]P. Liu and P. Wang, "Some q‐rung orthopair fuzzy aggregation operators and their applications to multiple‐ attribute decision making," International Journal of Intelligent Systems, vol. 33, no. 2, pp. 259-280, 2018.

[25]D. F. Li, "Extension of the LINMAP for multiattribute decision making under Atanassov's intuitionistic fuzzy environment," (in English), Fuzzy Optimization and Decision Making, vol. 7, no. 1, pp. 17-34, Mar 2008.

[26]M. E. Charlson, P. Pompei, K. L. Ales, and C. R. MacKenzie, "A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation," Journal of Chronic Diseases, vol. 40, no. 5, pp. 373-383, 1987/01/01/ 1987.