Machine learning models for predicting the diagnosis of liver disease

 Introduction: The liver is the most important organ of the body has a central role in metabolism. Liver disease cannot be easily discovered in the early stages, because even when the liver is damaged partially, it also can work truly, and this makes it difficult to diagnose. Automatic classification tools as a diagnostic tool can reduce the workload of doctors. Smart ways to detect liver disease classification used in this study consist of classifier and Naïve Bayes, Trees Random Forest, 1NN, AdaBoost, SVM. Materials and Methods: Our database was 583 patient records which they have been registered at university of California in 2013. For evaluate the proposed models, it is used K-fold cross validation. Five models of machine learning compare base on specificity, sensitivity, accuracy and area under ROC curve. Results: The accuracy of the five models, respectively, 55%, 72%, 64%, 70% and 71% respectively and area under the ROC curve of 0.72, 0.72, 0.59, and 0.67 is 0.5. Conclusion: Trees Random Forest model was the best model with the highest level of accuracy. The area under the ROC curve of Trees Random Forest and Naïve Bayes models have the largest area under the curve. Therefore Trees Random Forest model and predict the diagnosis of liver disease is recommended