hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

Abstract The paper focuses on Hepatitis C Virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a model called hyOPTGB, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as preprocessing step for scaling the dataset values and also, using the forward selection (FS) wrapped method to identify important features in the dataset. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also conducted a comparative study of the hyOPTGB model against other models proposed by different authors who used the same dataset.