A Neuro-Fuzzy-based Approach to Detect Liver Diseases

Abstract

The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results.