… The use of machine learning methods has improved predictive modelling in other studies in
… , adequately predicted SVR in these patients. The advantage of using machine learning over …

… Highly prevalent hepatitis B and hepatitis C virus (HBV and HCV) infections have been
reported among individuals with diabetes. Given the frequently asymptomatic nature of …

… This study suggests a prediction framework for the Hepatitis C virus that is based on machine
learning techniques. The authors made use of a dataset available on Kaggle. In this dataset…

… data mining using different machine learning techniques for … patients’ datasets and
predicting response of HCV patients … to predict response to treatment in patients with hepatitis C …

… qualified or not for its use. We have applied a variety of machine learning techniques such
as: RandomForest, KNN (K-Nearest-Neighbor), SVM (Support Vector Machine), and a neural …

… curative treatments, including liver resection (LR). We aimed at developing and validating
a machine-learning algorithm (ML) to predict which patients are sufficiently treated by LR. …

… Cured individuals predicted from the machine learning algorithms were removed from each
year’s estimates. The total number of individuals who remained HCV RNA positive and not …

… model for predicting therapeutic outcomes prior to treatment. In this section, the current
updates of ML algorithms are reviewed for various treatment modalities in HCC patients. …

… use of machine learning tools to predict 30-day and 90-day mortality in patients with alcoholic
hepatitis using … , random forest, support vector machine, and logistic regression models. …

… Machine learning algorithms also helps in the prediction of secondary structural features in
… An ensemble technique, based on two stochastic supervised machine learning algorithms …