Hepatitis Identification using Backward Elimination and Extreme Gradient Boosting Methods

Hepatitis Identification using Backward Elimination and Extreme Gradient Boosting Methods

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Background: Hepatitis is a contagious inflammatory read more 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 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 here as well as the use of feature average 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.