New Study Findings from National Institute of Technology Illuminate Research in Hepatitis (Hepatitis Identification using Backward Elimination and Extreme Gradient Boosting Methods).

A study conducted by the National Institute of Technology in Bandung, Indonesia, focused on the identification of hepatitis using backward elimination and extreme gradient boosting methods. Hepatitis is a contagious inflammatory disease of the liver that can be caused by viral infections, disease complications, alcohol, autoimmune diseases, and drug effects. The researchers found that using backward elimination in the XGBoost model led to faster training, improved accuracy, and decreased overfitting. The study achieved an accuracy of 98.958% and concluded that this approach could be beneficial in hepatitis identification. [Extracted from the article]