Journal article
Early lung cancer screening using double normalization-based multi-aggregation (DNMA) and Delphi methods with hesitant fuzzy information
Computers & industrial engineering, v 136, pp 453-463
Oct 2019
Abstract
•A hesitant fuzzy double normalization-based multi-aggregation method is proposed.•The criteria system for lung screening is constructed by the fuzzy Delphi method.•An example is provided to verify the applicability of the proposed method.•Comparative work and managerial implications from the case study are given.
Withthedevelopmentofeconomyandtheimprovementofpeople'slivingstandard, health has become one of the most important things that people pay attention to. Lung cancer, as one of the most serious malignant tumors threatening people's health, has a high mortality rate and a low cure rate for the reason that the early symptoms of it are not obvious. Usually, when a patient is found to have lung cancer, s/he is already at the middle or even the advanced stage. The early screening of lung cancer is of great significance for the effective treatment of lung cancer. The lung cancer screening process can be seen as a multi-criteria decision making problem. However, the nature of making a right decision for lung cancer screening is unstructured and complex since there are many factors that may affect such decisions. Furthermore, these factors are usually vague and difficult to evaluate precisely and numerically. The hesitant fuzzy set is a powerful tool to deal with uncertain and ambiguous information and has better applicability in quantifying such information. This study establishes a framework that uses the double normalization-based multi-aggregation method to solve the lung cancer screening problem. The critical factors for lung cancer screening are obtained by the fuzzy Delphi method. We adopt an illustrative example that is related to the lung cancer screening in a hospital of China to demonstrate the effectiveness of the proposed model. Furthermore, a comparison analysis is provided to verify the stability of the proposed method.
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Details
- Title
- Early lung cancer screening using double normalization-based multi-aggregation (DNMA) and Delphi methods with hesitant fuzzy information
- Creators
- Huchang Liao - Sichuan UniversityYilu Long - Sichuan UniversityMing Tang - Sichuan UniversityDalia Streimikiene - Lithuanian Sports UniversityBenjamin Lev - Drexel University
- Publication Details
- Computers & industrial engineering, v 136, pp 453-463
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000494891000037
- Scopus ID
- 2-s2.0-85069989424
- Other Identifier
- 991019168742504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Industrial