A knowledge elicitation framework in ranking healthcare providers using rough set with formal concept analysis. (10th February 2021)

• Record Type: Journal Article
• Authors: Mohapatro, Arati; Mahendran, S.K.; Das, Tapan Kumar
• Is Part Of: International journal of computational science and engineering. Volume 23:Number 4(2020)
• Page Start: 396
• Page End: 407
• Publication Date: 2021-02-10
• Subjects: rough set with intuitionistic fuzzy approximation space -- formal concept analysis -- FCA -- hospital ranking -- knowledge mining -- attribute exploration; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

A comparison of healthcare institutions by ranking involves generating their relative scores based on the infrastructure, process 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 is not equally important when it comes to ranking. Hence, the objective of this research is to explore the parameters which are vital 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 is discussed using AHA US News score data for cancer specialisation. The result signifies the connection between quality attributes and ranking. Finally, the leading attribute and its particular values are identified for different states of ranking.