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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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.K., Sarkar, M., Sahoo, J.P., 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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