… the easy to use and the interpretation of the models. Our finding suggests that random forest
model would be best option to implement a system for predicting fatty liver disease patients …

… Therefore, it is the key to establish the forecasting model to select easily available … prediction
indicators. In this study, based on random forest and logistic regression, a prediction model …

… models, ie help for Vector Machines, Decision Tree and Random Forest for the prediction of
liver disease… , and RF to predict or classify the liver disease by using different attributes. Fig.1 …

… for the prediction of liver disorder using Rapid Miner and IBM … We have developed a model
using Random Forests and … a model which aims at classifying the most severe disease …

… Logistic regression, Auto Neural, and random forest … of the used models to predict chronic
liver disease (CLD). The … logistic regression, K-NN, decision tree, random forest, and SVM. The …

… that Random forest was best for predicting the liver disease whether the … liver patient dataset
and we compared the different models in all the fields and we founded that Random Forest …

… Random forests average multiple deep decision trees… Decision Stump is the most powerful
classifier for this example. This result shows that the liver disease of a new patient is predicted …

… used to train classifier algorithms to predict liver disease with good … predicted the most
important features for liver disease detection with the highest accuracy over SVM, random forest…

… In the forest of trees has been the immediate connection … effective and precise predictions,
random forest inserts an … an increasingly precise model of liver disease prediction and …

… liver patient datasets are investigate for building classification models in order to predict
liver disease… In this paper the use of several classification (J.48, SVM, Random Forest, etc) …