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. 2025 Aug 13;15(1):29645.
doi: 10.1038/s41598-025-12296-w.

Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel-Alsina WASPAS method

Jabbar Ahmmad  1 , Meraj Ali Khan  2   3 , Ibrahim Aldayel  2 , Tahir Mahmood  4
Affiliations

Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel-Alsina WASPAS method

Jabbar Ahmmad et al. Sci Rep. .

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.

Keywords: AI-powered models; Aczel-Alsina t-norm and t-conorm; Complex fuzzy set; Disability conditions; WASPAS approach.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of the proposed study.
Fig. 2
Fig. 2
Flow chart of the proposed algorithm for the WASPAS approach.
Fig. 3
Fig. 3
Graphical representation of results in Table 4.
Fig. 4
Fig. 4
Graphical representation of results given in Table 5 for CFAAWA AOs.
Fig. 5
Fig. 5
Graphical representation of results given in Table 5 for CFAAWG AOs.
Fig. 6
Fig. 6
Graphical representation of data given in Table 6 for CFAAWA AOs.
Fig. 7
Fig. 7
Graphical representation of data given in Table 6 for CFAAWG AOs.
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