Background: Hepatitis is a contagious inflammatory disease of the liver and is a public health problem because it is easily transmitted.The main factors causing hepatitis are viral infections, disease complications, alcohol, autoimmune diseases, and drug effects.Some hepatitis variants such as B, C, and D can also cause liver cancer if left untreated.Objective: This research aims to determine the effect of Backward Elimination feature selection on the performance of hepatitis disease identification compared to cases where Backward Elimination is not applied.
Methods: XGBoost classification, capable of Remote Calibration Technology in Target RCS Time Domain Outfield Measurement handling machine learning problems, was utilized.Additionally, Backward Elimination was used as a featured selection to increase accuracy by reducing the number of less important features in the data classification process.Results: The results for training XGBoost model with Backward Elimination, and applying Random Search for hyperparameter optimization, achieved an accuracy of 98.958% at 0.
64 seconds.This performance was better than using Bayesian search, which produced the same accuracy of 98.958% but required a longer training time of 0.70 seconds.
Conclusion: The use of features obtained from Backward Elimination process as well as the use of feature average Semi-Analytical Option Pricing Under Double Heston Jump-Diffusion Hybrid Model values for missing value treatment, produced an accuracy of 98.958%.the precision in training XGBoost model with hyperparameter Bayesian search achieved accuracy, recall, and F1 score of 98.934%, 98.
934%, and 98.934%, respectively.Consequently, the use of Backward Elimination in XGBoost model led to faster training, improved accuracy, and decreased overfitting.Keywords: Hepatitis, Backward Elimination, XGBoost, Bayesian Search, Random Search.