Abstract
A comparison of healthcare institutions by ranking involves generating their relative scores based on infrastructure, process, services and other quality dynamics. Being a top-ranking institute depends on the overall score secured against the hospital quality parameters that are being assessed for ranking. However, each of the parameters does not equally important when it comes ranking. Hence, the objective of this research is to explore the parameters which are vital one as they significantly influence the ranking score. In this paper, a hybrid model is presented for knowledge extraction, which employs techniques of rough set on intuitionistic fuzzy approximation space (RSIFAS) for classification, Learning from Examples Module 2 (LEM2) algorithm for generating decision rules and formal concept analysis (FCA) for attribute exploration. The model can be implemented using a ranking scored data for any of the specialisations (cancer, heart disease, etc.). The result would signify the connection between quality attributes and ranking.
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References
Prabhakar Kalepu RN (2014) Service quality in healthcare sector: an exploratory study on hospitals. IUP J Mark Manage 13(1):7–28
Lupo T (2016) A fuzzy framework to evaluate service quality in the healthcare industry: an empirical case of public hospital service evaluation in Sicily. Appl Soft Comput 40:468–478
Das TK, Mohapatro A (2018) A system for diagnosing hepatitis based on hybrid soft computing techniques. Ind J Public Health Res Dev 9(2):235–239
Das TK (2016) Intelligent techniques in decision making: a survey. Ind J Sci Technol 9(12):1–6
Akbar S, Akram MU, Sharif M, Tariq A, Khan SA (2018) Decision support system for detection of hypertensive retinopathy using arteriovenous ratio. Artif Intell Med 90:15–24
Ladhari R (2009) A review of twenty years of SERVQUAL research. Int J Qual Serv Sci 1(2):172–198
Gadaras I, Mikhailov L (2009) An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif Intell Med 47:25–41
Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356
De SK (1999) Some aspects of fuzzy sets, rough sets and intuitionistic fuzzy sets. PhD Thesis, IIT, Kharagpur
Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Volume 83 of NATO advanced study institutes series. Springer, Berlin, pp 445–470
Yao Y (2004) Concept lattices in rough set theory. In: Proceedings of 2004 annual meeting of the North American fuzzy information processing society, vol 2, pp 796–801
Acharjya DP, Das TK (2017) A framework for attribute selection in marketing using rough computing and formal concept analysis. IIMB Manage Rev 29:122–135
Wang L, Liu X (2008) A new model of evaluating concept similarity. Knowl-Based Syst 21:842–846
Zhao Y, Halang WA, Wang X (2007) Rough ontology mapping in E-business integration. Stud Comput Intell 37:75–93
Grzymala-Busse JW, Stefanowski J (2001) Three discretization methods for rule induction. Int J Intell Syst 16:29–38
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Mohapatro, A., Mahendran, S.K., Das, T.K. (2021). A Knowledge Evocation Model in Grading Healthcare Institutions Using Rough Set and Formal Concept Analysis. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_32
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DOI: https://doi.org/10.1007/978-981-15-4218-3_32
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