Fuzzy logic applications in chronic disease in decision-making: a comprehensive healthcare review

Diagnosis as the initial step in medical practice is one of the most significant aspects of complex clinical decision making which is convoyed by some degree of uncertainty and perplexity. With massive volumes of medical data being collected, there is an urgent need to proficiently use this healthcare data throughout the globe. Among several computational conventions, fuzzy logic conveys an appealing way to control the imprecision and uncertainty in medical data. This article includes a systematic literature review for the investigation of fuzzy logic processes utilized in a variety of medical applications which has recently been published in highly reputed journals. Using PRISMA guidelines, a total of 25 studies selected from an initial pool of 340 recognized records. This review article discusses the role of fuzzy systems in the diagnosis of a variety of diseases including breast cancer, diabetes disease, cardiovascular disease, skin cancer, coronary artery disease, brain and tumor detection, chronic liver disease, fibrosis disease, brain disease, pulmonary and asthmatic disease, diabetes mellitus, musculoskeletal disorders, hypovolaemia disease, pancreatic cancer detection, thyroid, thalassemia disease, etc. Overall, this systematic review provides a suitable foundation for future study by highlighting research requirements in the field of disease diagnostics.