An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options

An interval neutrosophic linguistic multi-criteria group decision-making method and its... Selecting medical treatments is a critical activity in medical decision-making. Usually, medical treatments are selected by doctors, patients, and their families based on various criteria. Due to the subjectivity of decision-making and the large volume of information available, accurately and comprehensively evaluating information with traditional fuzzy sets is impractical. Interval neutrosophic linguistic numbers (INLNs) can be effectively used to evaluate information during the medical treatment selection process. In this study, a medical treatment selection method based on prioritized harmonic mean operators in an interval neutrosophic linguistic environment, in which criteria and decision-makers are assigned different levels of priority, is developed. First, the rectified linguistic scale functions of linguistic variables, new INLN operations, and an INLN comparison method are developed in order to prevent data loss and distortion during the aggregation process. Next, a generalized interval neutrosophic linguistic prioritized weighted harmonic mean operator and a generalized interval neutrosophic linguistic prioritized hybrid harmonic mean operator are developed in order to aggregate the interval neutrosophic linguistic information. Then, these operators are used to develop an interval neutrosophic linguistic multi-criteria group decision-making method. In addition, the proposed method is applied to a practical treatment selection method. Furthermore, the ranking results are compared to those obtained using a traditional approach in order to confirm the practicality and accuracy of the proposed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options

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Publisher
Springer London
Copyright
Copyright © 2016 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-016-2203-1
Publisher site
See Article on Publisher Site

Abstract

Selecting medical treatments is a critical activity in medical decision-making. Usually, medical treatments are selected by doctors, patients, and their families based on various criteria. Due to the subjectivity of decision-making and the large volume of information available, accurately and comprehensively evaluating information with traditional fuzzy sets is impractical. Interval neutrosophic linguistic numbers (INLNs) can be effectively used to evaluate information during the medical treatment selection process. In this study, a medical treatment selection method based on prioritized harmonic mean operators in an interval neutrosophic linguistic environment, in which criteria and decision-makers are assigned different levels of priority, is developed. First, the rectified linguistic scale functions of linguistic variables, new INLN operations, and an INLN comparison method are developed in order to prevent data loss and distortion during the aggregation process. Next, a generalized interval neutrosophic linguistic prioritized weighted harmonic mean operator and a generalized interval neutrosophic linguistic prioritized hybrid harmonic mean operator are developed in order to aggregate the interval neutrosophic linguistic information. Then, these operators are used to develop an interval neutrosophic linguistic multi-criteria group decision-making method. In addition, the proposed method is applied to a practical treatment selection method. Furthermore, the ranking results are compared to those obtained using a traditional approach in order to confirm the practicality and accuracy of the proposed method.

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Feb 8, 2016

References

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