A Survey on Prediction of Fatty Liver Disease by Using Machine Learning Techniques

Abstract

Fatty liver infection (FLD) is an umbrella term for some sorts of liver illnesses. As the name proposes, the fundamental clinical issue, is an excess of fat put away in liver cells. An early determination of patients with FLD will assist doctors with making a fitting technique for counteraction, early conclusion, and treatment. An ML model is planned which will help doctors in ordering theoretical patients, and cause another conclusion, to forestall and oversee FLD.This model represents the comparison of the four classification algorithms on different benchmark dataset to evaluate classification performance and predict fatty liver disease accurately.Likewise, it also presents the performance of the Fatty liver disease (FLD) prediction depends on following scaling factors such as Accuracy, Precision, Sensitivity(Recall), and Specificity. Usage of the ML model in the clinical setting could assist doctors with stratifying greasy liver patients for essential anticipation, in the early hours treatment, and the board.