Hybrid Evolutionary Algorithms and Fuzzy Sets for Optimization in Healthcare Decision Support Systems

The integration of Hybrid Evolutionary Algorithms (HEA) and Fuzzy Sets offers a powerful approach for optimizing decision-making processes in Healthcare Decision Support Systems (HDSS).This paper presents a novel framework that leverages the adaptive nature of fuzzy sets to manage uncertainty in medical data and the optimization capabilities of evolutionary algorithms to enhance decision accuracy.The proposed system focuses on feature selection, fuzzy rule generation, and performance optimization, resulting in improved prediction accuracy, reduced decision complexity, and enhanced handling of uncertain data.Testing and validation on healthcare datasets demonstrated the model's effectiveness, achieving higher accuracy, precision, and reliability compared to conventional methods.These results highlight the potential of this hybrid approach in making more informed and reliable clinical decisions.