Distance Measures-Based Decision-Making Under Neutrosophic Picture Fuzzy Environment for Respiratory Disease Diagnosis
DOI:
https://doi.org/10.65069/jessd1120252Keywords:
Neutrosophic picture fuzzy sets, Distance measure, Decision making, Respiratory disease diagnosisAbstract
Uncertainty and vagueness represent inherent characteristics of complex systems such as medical diagnosis. While various extensions of fuzzy set theory offer distinct advantages in characterizing uncertain information, the emerging neutrosophic picture fuzzy set (NPFS) provides superior modeling capability through its four-dimensional structure that independently represents truth, indeterminacy, falsity, and refusal degrees. However, research on dedicated distance measures for NPFSs remains underdeveloped. To address this limitation, this paper introduces some novel distance measures for NPFSs. We propose the Hamming-Hausdorff, Euclidean-Hausdorff, Hybrid Hamming, and Hybrid Euclidean distances. All proposed measures are rigorously proven to satisfy the fundamental metric axioms of boundedness, identity, symmetry, and triangle inequality, with weighted variants further developed to incorporate attribute importance. To validate their practical efficacy, we implement these measures within a minimum-distance-based decision-making framework applied to respiratory disease diagnosis. The results demonstrate that the proposed measures achieve superior discrimination accuracy and robustness.
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