Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications
Abstract Remote Patient Monitoring Systems (RPMS) are vital for tracking patients’ health outside clinical settings, such as at home or in long-term care facilities. Wearable sensors play a crucial role in these systems by continuously collecting and transmitting health data. However, selecting the...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00914-6 |
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| author | Asghar Khan Saeed Islam Muhammad Ismail Abdulaziz Alotaibi |
| author_facet | Asghar Khan Saeed Islam Muhammad Ismail Abdulaziz Alotaibi |
| author_sort | Asghar Khan |
| collection | DOAJ |
| description | Abstract Remote Patient Monitoring Systems (RPMS) are vital for tracking patients’ health outside clinical settings, such as at home or in long-term care facilities. Wearable sensors play a crucial role in these systems by continuously collecting and transmitting health data. However, selecting the optimal sensor is challenging due to the wide variety of available options and diverse patient needs. To address this paper, introduce score and accuracy functions for Triangular Fermatean Fuzzy Numbers (TFFNs) and propose a novel Triangular Fermatean Fuzzy Sugeno–Weber Weighted Average (TFFSWWA) aggregation operator. In this paper establish key properties of TFFSWWA, confirming its ability to manage fuzzy uncertainty effectively. Using TFFSWWA, we develop an improved Evaluation based on Distance from Average Solution (EDAS) method for multi-criteria group decision-making (MCGDM) under TFFN settings. A case study on wearable sensor selection demonstrates the proposed model’s efficiency. We present an algorithm and a flowchart to guide the decision-making process, alongside a computational example that verifies the method’s reliability. Sensitivity analysis and comparison with existing methods show that the proposed approach improves decision accuracy and stability, highlighting its practical utility in healthcare decision-making. |
| format | Article |
| id | doaj-art-adbb0c18e96741578245a08d03eb3c10 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-adbb0c18e96741578245a08d03eb3c102025-08-20T03:38:16ZengNature PortfolioScientific Reports2045-23222025-07-0115112210.1038/s41598-025-00914-6Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applicationsAsghar Khan0Saeed Islam1Muhammad Ismail2Abdulaziz Alotaibi3Department of Mathematics, Abdul Wali Khan UniversityDepartment of Mechanical Engineering, Prince Mohammad Bin Fahd UniversityDepartment of Mathematics, Abdul Wali Khan UniversityDepartment of Mathematics, College of Science and Humanities, Prince Sattam Bin Abdulaziz UniversityAbstract Remote Patient Monitoring Systems (RPMS) are vital for tracking patients’ health outside clinical settings, such as at home or in long-term care facilities. Wearable sensors play a crucial role in these systems by continuously collecting and transmitting health data. However, selecting the optimal sensor is challenging due to the wide variety of available options and diverse patient needs. To address this paper, introduce score and accuracy functions for Triangular Fermatean Fuzzy Numbers (TFFNs) and propose a novel Triangular Fermatean Fuzzy Sugeno–Weber Weighted Average (TFFSWWA) aggregation operator. In this paper establish key properties of TFFSWWA, confirming its ability to manage fuzzy uncertainty effectively. Using TFFSWWA, we develop an improved Evaluation based on Distance from Average Solution (EDAS) method for multi-criteria group decision-making (MCGDM) under TFFN settings. A case study on wearable sensor selection demonstrates the proposed model’s efficiency. We present an algorithm and a flowchart to guide the decision-making process, alongside a computational example that verifies the method’s reliability. Sensitivity analysis and comparison with existing methods show that the proposed approach improves decision accuracy and stability, highlighting its practical utility in healthcare decision-making.https://doi.org/10.1038/s41598-025-00914-6Triangular Fermatean fuzzy numberSugeno–Weber aggregation operatorRemote Patient Monitoring SystemsDecision support system |
| spellingShingle | Asghar Khan Saeed Islam Muhammad Ismail Abdulaziz Alotaibi Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications Scientific Reports Triangular Fermatean fuzzy number Sugeno–Weber aggregation operator Remote Patient Monitoring Systems Decision support system |
| title | Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications |
| title_full | Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications |
| title_fullStr | Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications |
| title_full_unstemmed | Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications |
| title_short | Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications |
| title_sort | development of a triangular fermatean fuzzy edas model for remote patient monitoring applications |
| topic | Triangular Fermatean fuzzy number Sugeno–Weber aggregation operator Remote Patient Monitoring Systems Decision support system |
| url | https://doi.org/10.1038/s41598-025-00914-6 |
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