Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method
Abstract Tracking the development of disability conditions presents significant challenges due to uncertainty, imprecision, and dynamic health progression patterns. Traditional multi-criteria decision-making (MCDM) techniques often struggle with such complex and fuzzy medical data. To address this g...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12296-w |
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| author | Jabbar Ahmmad Meraj Ali Khan Ibrahim Aldayel Tahir Mahmood |
| author_facet | Jabbar Ahmmad Meraj Ali Khan Ibrahim Aldayel Tahir Mahmood |
| author_sort | Jabbar Ahmmad |
| collection | DOAJ |
| description | Abstract Tracking the development of disability conditions presents significant challenges due to uncertainty, imprecision, and dynamic health progression patterns. Traditional multi-criteria decision-making (MCDM) techniques often struggle with such complex and fuzzy medical data. To address this gap, we propose a novel classification framework based on Tamir’s complex fuzzy Aczel-Alsina weighted aggregated sum product assessment (WASPAS) approach. This hybrid model incorporates complex fuzzy logic to handle multidimensional uncertainty and utilizes the Aczel-Alsina function for flexible aggregation. We apply this method to evaluate and classify AI-powered predictive models used for monitoring disability progression. The proposed framework not only improves classification accuracy but also enhances decision support in healthcare planning. A case study validates the robustness, sensitivity, and effectiveness of the proposed method in real-world disability tracking scenarios. |
| format | Article |
| id | doaj-art-a3ff00b19a22461385209da5e3405d4b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a3ff00b19a22461385209da5e3405d4b2025-08-20T03:05:26ZengNature PortfolioScientific Reports2045-23222025-08-0115111810.1038/s41598-025-12296-wClassifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS methodJabbar Ahmmad0Meraj Ali Khan1Ibrahim Aldayel2Tahir Mahmood3Department of Mathematics and Statistics, International Islamic University IslamabadDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, International Islamic University IslamabadAbstract Tracking the development of disability conditions presents significant challenges due to uncertainty, imprecision, and dynamic health progression patterns. Traditional multi-criteria decision-making (MCDM) techniques often struggle with such complex and fuzzy medical data. To address this gap, we propose a novel classification framework based on Tamir’s complex fuzzy Aczel-Alsina weighted aggregated sum product assessment (WASPAS) approach. This hybrid model incorporates complex fuzzy logic to handle multidimensional uncertainty and utilizes the Aczel-Alsina function for flexible aggregation. We apply this method to evaluate and classify AI-powered predictive models used for monitoring disability progression. The proposed framework not only improves classification accuracy but also enhances decision support in healthcare planning. A case study validates the robustness, sensitivity, and effectiveness of the proposed method in real-world disability tracking scenarios.https://doi.org/10.1038/s41598-025-12296-wDisability conditionsAI-powered modelsAczel-Alsina t-norm and t-conormComplex fuzzy setWASPAS approach |
| spellingShingle | Jabbar Ahmmad Meraj Ali Khan Ibrahim Aldayel Tahir Mahmood Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method Scientific Reports Disability conditions AI-powered models Aczel-Alsina t-norm and t-conorm Complex fuzzy set WASPAS approach |
| title | Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method |
| title_full | Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method |
| title_fullStr | Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method |
| title_full_unstemmed | Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method |
| title_short | Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method |
| title_sort | classifying ai powered prediction models for disability progression using the tamir based complex fuzzy aczel alsina waspas method |
| topic | Disability conditions AI-powered models Aczel-Alsina t-norm and t-conorm Complex fuzzy set WASPAS approach |
| url | https://doi.org/10.1038/s41598-025-12296-w |
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