@article{2020,title={Diagnosis of Liver Disease with Machine Learning Algortihms},abstractNode={Liver diseases are among quite common diseases seen worldwide. Liver diseases can pose great dangers in the body. For these reasons, liver disease Nonalcoholic fatty liver, alcoholic liver fatty, hepatitis A, hepatitis B, hepatitis C, hepatitis D, liver enlargement, liver cancer, liver and intrahepatic bile duct cancer, liver failure and cirrhosis as early as possible diagnosis and its treatment is vital. Traditional diagnostic methods are still used in medicine. However, today, thanks to the developing artificial intelligence technologies, powerful tools can be provided to support physicians in disease diagnosis, detection and treatment processes. In this study, using WEKA data mining tool by means of J48, Logistic Model Tree (LMT), Decision Stump, Hoeffding Tree, REP Tree, Random Forest, Random Tree and IBk machine learning algorithms are studied on Liver Patient Data Set (ILPD). With these algorithms, the best diagnostic result was tried to be reached. In order to evaluate the performance and success of the machine learning algorithms used in the study, firstly the confusion matrices were created and then the calculations were made according to the accepted values of the general validity in medicine. Performances of these algorithms on ILDP data set were calculated according to accuracy, sensitivity, specificity, ROC, MCC, recall, precision and F-Measure criteria respevtively. The best models obtained from machine learning algorithms can be used to create inference engines of intelligent systems to be developed for diagnosis and treatment. The obtained models have potential to create substructure of a smart system that enable to be detected the worldwide common liver diseases in early phase in all health institutions.},author={Aytürk KELEŞ-Özden Burcu KARSLI - Ali KELEŞ},year={2020},journal={Turkish Studies-Information Technologies and Applied Sciences}}