Hybrid fuzzy soft set and artificial intelligence model for healthcare decision support systems
DOI:
https://doi.org/10.64171/JAES.6.3.19-22Keywords:
Fuzzy soft sets, Artificial Intelligence, Clinical decision support systems, Multi-criteria decision making, Healthcare optimizationAbstract
Healthcare decision-making environments are inherently characterized by uncertainty, vagueness, imprecision, and multi-criteria complexity. Traditional deterministic approaches are often inadequate in handling such ambiguity. Fuzzy soft set theory, developed from the integration of fuzzy set theory and soft set theory, has demonstrated effectiveness in modeling parameterized uncertainty. However, standalone fuzzy soft models lack adaptive learning capabilities. This study proposes a hybrid Fuzzy Soft Set–Artificial Intelligence (FSS–AI) framework for clinical decision support systems (CDSS). The proposed model integrates fuzzy soft decision matrices with machine learning-based weight optimization to enhance diagnostic accuracy, treatment selection, and healthcare resource allocation. A healthcare treatment selection case study is presented to demonstrate the applicability of the model. The results indicate that the hybrid framework improves interpretability, scalability, and predictive reliability compared to classical fuzzy soft set approaches.
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