Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis
In recent days, due to the complexities of different diseases of similar types, it becomes very difficult to diagnose an accurate type of disease, and so medical diagnosis becomes a difficult task for the experts working in health departments. Many researchers try to develop new methods and techniqu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10015758/ |
| _version_ | 1828049118432329728 |
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| author | Tahir Mahmood Jabbar Ahmmad Zeeshan Ali Miin-Shen Yang |
| author_facet | Tahir Mahmood Jabbar Ahmmad Zeeshan Ali Miin-Shen Yang |
| author_sort | Tahir Mahmood |
| collection | DOAJ |
| description | In recent days, due to the complexities of different diseases of similar types, it becomes very difficult to diagnose an accurate type of disease, and so medical diagnosis becomes a difficult task for the experts working in health departments. Many researchers try to develop new methods and techniques to over the difficulties that come across in the way of medical diagnosis. In this paper, we try to develop some novel techniques that will help experts to diagnose diseases accurately. Based on a more advanced structure of intuitionistic fuzzy rough sets, in this article, we establish confidence-level intuitionistic fuzzy average/geometric aggregation operators to incorporate the familiarity degree of experts with evaluated objects for an initial assessment while intuitionistic fuzzy rough average/geometric aggregation operators cannot do so. Moreover, we have given some basic properties of the initiated operators. To show the effective use of these operators we have proposed an algorithm with an illustrative example. Furthermore, based on the intuitionistic fuzzy rough model, we have also established a medical diagnosis model to incorporate the difficulty that occurs in the diagnosis of disease. Furthermore, a comparative analysis demonstrates the efficiency of our proposed methods. |
| first_indexed | 2024-04-10T19:07:17Z |
| format | Article |
| id | doaj.art-ef1906c767a84c2abff61e1bfa7d9cdb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T19:07:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ef1906c767a84c2abff61e1bfa7d9cdb2023-01-31T00:00:35ZengIEEEIEEE Access2169-35362023-01-01118674868810.1109/ACCESS.2023.323641010015758Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical DiagnosisTahir Mahmood0https://orcid.org/0000-0002-3871-3845Jabbar Ahmmad1https://orcid.org/0000-0003-4599-2085Zeeshan Ali2Miin-Shen Yang3https://orcid.org/0000-0002-4907-3548Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, PakistanDepartment of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, PakistanDepartment of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, PakistanDepartment of Applied Mathematics, Chung Yuan Christian University, Zhongli, Taoyuan, TaiwanIn recent days, due to the complexities of different diseases of similar types, it becomes very difficult to diagnose an accurate type of disease, and so medical diagnosis becomes a difficult task for the experts working in health departments. Many researchers try to develop new methods and techniques to over the difficulties that come across in the way of medical diagnosis. In this paper, we try to develop some novel techniques that will help experts to diagnose diseases accurately. Based on a more advanced structure of intuitionistic fuzzy rough sets, in this article, we establish confidence-level intuitionistic fuzzy average/geometric aggregation operators to incorporate the familiarity degree of experts with evaluated objects for an initial assessment while intuitionistic fuzzy rough average/geometric aggregation operators cannot do so. Moreover, we have given some basic properties of the initiated operators. To show the effective use of these operators we have proposed an algorithm with an illustrative example. Furthermore, based on the intuitionistic fuzzy rough model, we have also established a medical diagnosis model to incorporate the difficulty that occurs in the diagnosis of disease. Furthermore, a comparative analysis demonstrates the efficiency of our proposed methods.https://ieeexplore.ieee.org/document/10015758/Fuzzy setsintuitionistic fuzzy setsrough setsintuitionistic fuzzy rough setsconfidencelevel aggregation operatorsmedical diagnosis |
| spellingShingle | Tahir Mahmood Jabbar Ahmmad Zeeshan Ali Miin-Shen Yang Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis IEEE Access Fuzzy sets intuitionistic fuzzy sets rough sets intuitionistic fuzzy rough sets confidencelevel aggregation operators medical diagnosis |
| title | Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis |
| title_full | Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis |
| title_fullStr | Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis |
| title_full_unstemmed | Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis |
| title_short | Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis |
| title_sort | confidence level aggregation operators based on intuitionistic fuzzy rough sets with application in medical diagnosis |
| topic | Fuzzy sets intuitionistic fuzzy sets rough sets intuitionistic fuzzy rough sets confidencelevel aggregation operators medical diagnosis |
| url | https://ieeexplore.ieee.org/document/10015758/ |
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