Fuzzy Soft Set Theory Applications in Medical Diagnosis: A Comprehensive Review and the Roadmap for Future Studies

Abstract

This paper provides a literature review addressing the use of soft sets in medical diagnosis. Distinguishing itself from the existing literature, the study offers a comprehensive analysis of how fuzzy soft sets can be integrated into diagnostic processes, highlighting a novel fusion of fuzzy and soft sets in medical applications. Any soft set on a countably infinite universe can be regarded as a fuzzy set, recognizing the limitations of traditional diagnostic tools in dealing with vague and incomplete information, our research aims to utilize the flexibility and comprehensiveness of fuzzy soft sets to enhance decision-making accuracy in medical scenarios. The primary objective of this research is to present a thorough and critical analysis of fuzzy soft set theory in medical diagnosis, aiming to establish it as a fundamental approach in the field. By combining all of these results, we can generate a comprehensive picture of the connections between the many theories that account for fuzziness and imprecision, which helps to fill in the blanks left by recent surveys. The review will focus on identifying the main research trends in this field: the primary research topics that soft set theory addresses in medical applications, the community is currently facing and the major theoretical concepts used to study these topics. The primary objective of this review is to assist in the identifying of emerging research directions. Some of these trends are promising and will help shape a new role for soft set theory in the area of medical applications. The fusion of fuzzy and soft sets in medical applications represents a crucial and necessary stage in the research and diagnosis procedures. Key results include the study on development of algorithms and models that outperform traditional methods in accuracy and reliability.