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Authors' ORCIDs

Abduallah Gamal: https://orcid.org/0000-0002-3819-0714

Article Type

Research Article

Abstract

Functional disability assessment is a multidimensional problem because individuals may experience different levels of difficulty across daily activities such as walking, standing, dressing, eating, grasping objects, and social participation. This paper proposes NIFDA, a data-driven neutrosophic multi-criteria intelligent system for functional disability severity assessment. The proposed framework represents each functional response through three components: confirmed limitation, indeterminacy, and preserved functional ability. This allows the model to handle valid responses, uncertain information, and missing or non-informative data without forcing them into a single crisp score. To reduce dependence on subjective expert weighting, NIFDA derives criterion weights objectively using a hybrid CRITIC–MEREC mechanism. A consensus ranking score is then computed to produce an individualized Functional Disability Severity Index and an interpretable severity ranking. The framework is evaluated using real NHANES 2017–2018 Physical Functioning data, including 2918 adult respondents and 20 functional criteria. The results show that NIFDA can identify high-severity functional profiles, explain the dominant limitation domains, and remain stable under sensitivity and robustness tests. The proposed system is intended as a decision-support tool for functional screening, rehabilitation prioritization, and population-level disability analysis, not as a replacement for clinical judgment.

Keywords

Functional disability assessment, Neutrosophic systems, Multi-criteria decision-making, Objective weighting, CRITIC, MEREC, Functional severity, NHANES, Decision support

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