Comparative Analysis of Diagnosis of Non-Alcoholic Fatty Liver Disease (NAFLD) Using Machine Learning Algorithms

Liver disease is one of the leading cause of deaths in the world. It could be reversed if detected at early stage and many lives could be saved thereby. Therefore, it is crucial to detect the liver disease at an early stage. The primary goal of this review is to present a thorough summary of recent research, which have been carried out using machine learning (ML) techniques. These techniques have been developed by researchers to help healthcare professionals to detect non-alcoholic fatty liver disease (NAFLD). In this study, several ML techniques viz., supervised, unsupervised, and semi-supervised reinforcement learning are covered, whose performance is compared in terms of sensitivity, accuracy, and precision. The prime objective of this study is to present a comparative analysis of different ML methods for the diagnosis and early prediction of liver disease in the medical images.