Advances in Fuzzy and Neuro-Fuzzy Approaches for Liver Disease Detection

Shikha Verma

Abstract: Liver diseases require early and correct diagnosis to be effectively managed and treated. Past years have witnessed the development of fuzzy logic and neuro-fuzzy systems as promising methods of computational processing of the inherent uncertainty and imprecision of medical data. In this review, the latest developments in fuzzy and neuro-fuzzy methods used to detect liver diseases are thoroughly analyzed. We discuss different models, such as fuzzy rule-based systems, adaptive neuro-fuzzy inference systems (ANFIS) and hybrid neuro-fuzzy systems, their methodologies, performance and clinical applicability. The main issues, including the choice of features, the quality of data, and interpretability, are addressed as well as the benefits of using fuzzy-based methods over the conventional diagnostic tools are compared. Lastly, the future perspectives are described, with the focus on the fusion of the advanced machine learning methods and fuzzy logic to improve the accuracy of diagnostic and decision support in liver diseases management. This review is expected to equip the researchers and clinicians with a unified knowledge of the state-of-the-art fuzzy and neuro-fuzzy applications in the detection of liver disease.

Keywords: Fuzzy logic, Neuro-fuzzy systems, Adaptive neuro-fuzzy inference system (ANFIS)