Vol. 10, Issue 2, Part B (2024)

The landscape of medical diagnostics has witnessed a paradigm shift with the advent of fuzzy logic methods, offering a nuanced and adaaptable approach to handle the inherent uncertainty and imprecision in healthcare data. This meta-analysis seeks to demystify the diagnostic applicability of fuzzy logic methods in various diseases, providing a comprehensive synthesis of existing research findings. A systematic review was conducted across a diverse range of medical disciplines, encompassing studies that employed fuzzy logic methods for disease diagnosis. The meta-analysis involves the synthesis of data from numerous research articles, clinical trials, and case studies, focusing on the performance, reliability, and effectiveness of fuzzy logic-based diagnostic systems. The findings reveal a substantial body of evidence supporting the efficacy of fuzzy logic methods in disease diagnosis. Fuzzy logic has demonstrated a remarkable ability to model complex and dynamic relationships within medical datasets, accommodating uncertainty and imprecision inherent in diagnostic parameters. The meta-analysis highlights the versatility of fuzzy logic in handling diverse data types, including clinical, imaging, and molecular data, thereby providing a holistic perspective on its diagnostic applicability. Moreover, this study explores the impact of fuzzy logic-based diagnostic systems on clinical decision-making, emphasizing the potential for enhanced accuracy, early detection, and personalized treatment strategies. The meta-analysis also sheds light on the challenges and limitations associated with fuzzy logic methods in disease diagnosis, offering insights into areas for further refinement and improvement.