This paper presents a comprehensive study of defuzzification methods designed specifically for intuitionistic fuzzy sets (IFS), which extend traditional fuzzy sets by incorporating an additional dimension of uncertaintyhesitation.The research analyzes and compares three main classes of defuzzification techniques: centroid-based, score function-based, and hesitation-driven methods.Each approach is evaluated in terms of its ability to transform fuzzy data, described by membership, non-membership, and hesitation degrees, into meaningful crisp outputs that support more accurate decisionmaking.A significant contribution of this study is the application of the centroid-based defuzzification method in the medical domain, specifically for cardiovascular disease prediction.The paper outlines a framework where patient data is modeled using IFS and converted into a single risk score, enhancing clinical decision-making by quantifying uncertainty in diagnostic parameters.The results demonstrate that incorporating hesitation into defuzzification processes can yield more nuanced, reliable outcomes compared to traditional methods.The findings underscore the importance of selecting appropriate defuzzification strategies depending on the decision context and highlight the potential for applying IFSbased models in other complex and uncertain environments.The paper concludes with suggestions for future research, including hybrid techniques and broader applications in decision-support systems.
| Jurnal | ТАТУ хабарлари |
|---|---|
| Nashr sanasi | 2024-12-29 |
| DOI | 10.61663/244tuitmct6 |
DOI: 10.61663/244tuitmct6 · Maqolaning asl sahifasi
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