Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

Area of Science:

• Medical Informatics
• Computational Intelligence
• Diagnostic Systems

Background:

• Medical diagnosis involves complex decision-making often fraught with ambiguity and uncertainty.
• Fuzzy logic methods offer a robust approach to mitigate this inherent ambiguity in clinical practice.
• Existing reviews on fuzzy logic in medical diagnosis are outdated, necessitating a current systematic evaluation.

Purpose of the Study:

• To systematically review and assess the contribution of fuzzy logic methods in disease diagnosis across diverse medical disciplines.
• To identify trends, research gaps, and the impact of fuzzy logic applications in improving diagnostic accuracy.
• To provide an updated perspective on the utility of fuzzy logic in addressing diagnostic challenges.

Main Methods:

• A systematic review and meta-analysis adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
• Inclusion and exclusion criteria were applied to a comprehensive search of eight scientific databases.
• Eligible articles were classified and analyzed based on applied fuzzy methods, medical disciplines, system design, and diagnostic outcomes.

Main Results:

• The study confirms the effectiveness of various fuzzy logic methods in enhancing disease diagnosis.
• Identified key areas and disease types that have received significant research focus.
• Provided insights into diagnostic aspects within medical disciplines that may be underexplored.

Conclusions:

• This systematic review establishes a foundation for future research in medical disease diagnosis.
• Highlights the need for continued investigation into fuzzy logic applications to refine diagnostic processes.
• Identifies research needs and potential future directions in the domain of computational intelligence for medical diagnosis.