METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.
RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.
CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.
METHODS: All publications related to hepatitis B reactivation with the use of immunosuppressive therapy since 1975 were reviewed. Advice from key opinion leaders in member countries/administrative regions of Asian-Pacific Association for the study of the liver was collected and synchronized. Immunosuppressive therapy was risk-stratified according to its reported rate of hepatitis B reactivation.
RECOMMENDATIONS: We recommend the necessity to screen all patients for hepatitis B prior to the initiation of immunosuppressive therapy and to administer pre-emptive nucleos(t)ide analogues to those patients with a substantial risk of hepatitis and acute-on-chronic liver failure due to hepatitis B reactivation.