A picture fuzzy set-based MADM Framework for personalized hospital recommendations using patient feedback

Abstract

This paper presents a Picture fyzzy entropy based Multi-Attribute Decision-Making (MADM) approach to generate personalized hospital recommendations from patient feedback. Patient evaluations are modelled using picture fuzzy sets to capture truth, indeterminacy, and falsity. Picture fuzzy entropy is applied to derive objective attribute weights for factors such as infrastructure, staff competency, cost, patient experience, and quality of care. The proposed method produces robust and patient-cantered References hospital rankings, outperforming traditional fuzzy MADM techniques. A real-world case study confirms its effectiveness in supporting data-driven hospital decision-making.