期刊
SOFT COMPUTING
卷 25, 期 6, 页码 4213-4241出版社
SPRINGER
DOI: 10.1007/s00500-020-05437-y
关键词
Intelligent healthcare management; Interval-valued fuzzy soft sets; Score function; CoCoSo; CRITIC
资金
- National Natural Science Foundation of China [61806213]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences [18YJCZH054]
- Natural Science Foundation of Guangdong Province [2018A030307033, 2018A0303130274]
- Social Science Foundation of Guangdong Province [GD18CFX06]
- Special Innovation Projects of Universities in Guangdong Province [KTSCX205]
The study focuses on addressing uncertainties in intelligent healthcare management through the use of interval-valued fuzzy soft sets. By introducing a new score function and utilizing the CRITIC method to calculate objective weights, a decision-making algorithm called CoCoSo is developed to effectively differentiate alternatives with high accuracy.
The intelligent healthcare management is of great concern to mobilize the enthusiasm of individuals and groups, and effectively use limited resources to achieve maximum health improvement by AI technology. When considering the intelligent healthcare management evaluation, the primary issues involve many uncertainties. Interval-valued fuzzy soft set, depicted by membership degree with interval form, is a more resultful means for capturing uncertainty. In this paper, the comparison issue in interval-valued fuzzy soft environment is disposed of by proposing novel score function. Later, some new properties for interval-valued fuzzy soft matrix are investigated in detail. Moreover, the objective weight is calculated by CRITIC (Criteria Importance Through Inter-criteria Correlation) method. Meanwhile, the combined weight is determined by reflecting both subjective weight and the objective weight. Then, interval-valued fuzzy soft decision-making algorithm-based CoCoSo (Combined Compromise Solution) is developed. Lastly, the validity of algorithm is expounded by the healthcare management industry evaluation issue, along with their sensitivity analysis. The main characteristics of the presented algorithm are: (1) without counterintuitive phenomena; (2) no division by zero problem; (3) have strong ability to distinguish alternatives.
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