A Complex Neutrosophic Soft Rough Framework for Efficient Multi-Attribute Decision-Making Under Uncertainty

Abstract

Decision-making plays a vital role across diverse domains including risk assessment, engineering, medical diagnosis, artificial intelligence, and machine learning particularly in environments characterized by incomplete, inconsistent information. Within the health care industry, especially in medical diagnosis, select- ing the most suitable alternative based on multiple attributes presents a significant challenge. Conventional decision-making frameworks grounded in classical set theory such as fuzzy sets and soft sets often fall short when addressing highly complex and uncertain scenarios. To overcome these limitations, this study presents an advanced model based on complex neutrosophic soft set with roughness structures. This approach combines the strengths of neutrosophic sets, soft sets, and rough sets within a complex domain thereby improving the han- dling of uncertainty and enhancing decision-making accuracy. In addition, a novel decision-making algorithm is developed within this framework to streamline the selection process by reducing redundant computations and efficiently identifying optimal alternatives. Comparative analysis with existing models and algorithms highlights the validity and superior performance of the proposed framework.