Assessing the Effectiveness of Ensemble Learning Models for Hepatitis C Detection through Advanced Machine Learning Techniques

Abstract

This paper investigates the potential benefits of utilizing advanced methods of machine learning to enhance Hepatitis C diagnosis tools. We used a publically available dataset to test different ensemble learning techniques, such as Grid Search and Random Search to optimize the parameters of Random Forest, Gradient Boosting, Bagging, XGBoost, and stacking. We evaluated the performance of the model using Cohen's Kappa, F1 score, accuracy, precision, and recall. With 92.37% accuracy, 83.85% precision, and a 70.17% F1 score, XGBoost with Random Search demonstrated the best performance. The results show that medical diagnostics can be improved and that methods of ensemble learning are useful for early Hepatitis C identification.