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      Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

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          Abstract

          The paper focuses on the 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 hyOPTGB model, 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 a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. 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 compared the hyOPTGB model against other models proposed by authors who used the same dataset.

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                Author and article information

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                Journal
                DIAGC9
                Diagnostics
                Diagnostics
                MDPI AG
                2075-4418
                November 2023
                November 13 2023
                : 13
                : 22
                : 3439
                Article
                10.3390/diagnostics13223439
                4766dfff-84a0-425b-b53e-045db9cf399e
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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