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.
METHODOLOGY: This is a prospective study where patients (n=119) blood was tested for anti-HAVIgG and CYP3A4*18 polymorphism.
RESULTS: The overall anti-HAV seroprevalence was 88.2%. The etiology of CLD was hepatitis B in 96 patients (80.7%) and hepatitis C in 23 patients (19.3%). There was a significant increase in the age of the prevalence of this disease after 30 years of age (p=0.008). CYP3A4*18 polymorphism was detected in 3 (2.5%) of the patients with chronic liver disease. However, there was no significant association between CP3A4*18 mutation and anti-HAV serology.
CONCLUSIONS: Age was the most important factor in determining anti-HAV positivity. It is concluded that CYP3A4*18 genetic polymorphism does not play a main role in influencing the seroprevalence of anti-hepatitis A among chronic viral hepatitis B and C liver disease patients.
RESULTS: cSC binds > 0.06 μg/ml of purified human and mouse pIgA with negligible cross-reactivity against IgM and IgA. Virus-specific pIgA was significantly higher in serum of acute HAV (n = 6) and HEV (n = 12) patients than uninfected samples (HEV: p