… of different Machine Learning algorithms in order to reduce the high cost of chronic liver
disease diagnosis by prediction. In this work, we used six algorithms Logistic Regression, K …

… This thesis analyzed the liver disease using algorithms such as J48, MLP, SVM, Random
Forest, and Bayesnet Classification. These algorithm gives various result based on PSO …

… machine learning algorithms’ performances and decrease the cost of protracted liver disease,
this study used algorithms… The algorithms were analyzed in the accuracy, precision, recall, …

… is to be able to predict the occurrence of liver disease in a sample dataset/ training data set,
in … to the greater magnitude of accuracy using the appropriate machine learning algorithm. …

… Machine learning The primary objective of this study was, to select prognostic factors for
predicting fatty liver disease using classification machine learning models. In this process, we …

… The presence of enzymes in the blood can be used to identify liver disease. In this paper
we are using liver patient dataset to predict whether the patient is having liver disease or not. …

… In this work, various strategies for predicting liver disease in humans are analyzed using ML
techniques. The algorithms like KNN, DT and ANFIS are implemented, and the performance …

… In this research, four (4) machine learning algorithms are applied on IlPD … deep learning
and transfer learning algorithms with various feature selection techniques for classifying liver …

… to predict the disease. The patient is advised to undergo LFT if predicted with some liver …
in foresee the presence of liver disease using different classification algorithms. The dataset …

… algorithm with the use of machine learning for detection of … The result found from the liver
disease dataset by using Weka tool … The objective of this study is liver disease prediction using …