Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

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 pape...

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Vydáno v:	Diagnostics (Basel) Ročník 13; číslo 22; s. 3439
Hlavní autoři:	Elshewey, Ahmed M., Shams, Mahmoud Y., Tawfeek, Sayed M., Alharbi, Amal H., Ibrahim, Abdelhameed, Abdelhamid, Abdelaziz A., Eid, Marwa M., Khodadadi, Nima, Abualigah, Laith, Khafaga, Doaa Sami, Tarek, Zahraa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.11.2023
Témata:	Accuracy Algorithms Biopsy Blood Classification Datasets Disease Feature selection Forecasts and trends Genotype & phenotype gradient boosting (GB) Hepatitis C Hepatitis C virus hepatitis C virus (HCV) hyperparameters Infection Infections Inflammation Liver cancer Liver cirrhosis Machine learning Medical research Medicine, Experimental Optimization OPTUNA Egypt Taiwan
ISSN:	2075-4418, 2075-4418
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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.
AbstractList	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. 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.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.
Audience	Academic
Author	Tarek, Zahraa Alharbi, Amal H. Ibrahim, Abdelhameed Eid, Marwa M. Shams, Mahmoud Y. Abdelhamid, Abdelaziz A. Abualigah, Laith Khafaga, Doaa Sami Elshewey, Ahmed M. Khodadadi, Nima Tawfeek, Sayed M.
Author_xml	– sequence: 1 givenname: Ahmed M. orcidid: 0000-0002-3048-1920 surname: Elshewey fullname: Elshewey, Ahmed M. – sequence: 2 givenname: Mahmoud Y. orcidid: 0000-0003-3021-5902 surname: Shams fullname: Shams, Mahmoud Y. – sequence: 3 givenname: Sayed M. surname: Tawfeek fullname: Tawfeek, Sayed M. – sequence: 4 givenname: Amal H. surname: Alharbi fullname: Alharbi, Amal H. – sequence: 5 givenname: Abdelhameed orcidid: 0000-0002-8352-6731 surname: Ibrahim fullname: Ibrahim, Abdelhameed – sequence: 6 givenname: Abdelaziz A. orcidid: 0000-0001-7080-1979 surname: Abdelhamid fullname: Abdelhamid, Abdelaziz A. – sequence: 7 givenname: Marwa M. surname: Eid fullname: Eid, Marwa M. – sequence: 8 givenname: Nima orcidid: 0000-0002-8348-6530 surname: Khodadadi fullname: Khodadadi, Nima – sequence: 9 givenname: Laith orcidid: 0000-0002-2203-4549 surname: Abualigah fullname: Abualigah, Laith – sequence: 10 givenname: Doaa Sami orcidid: 0000-0002-9843-6392 surname: Khafaga fullname: Khafaga, Doaa Sami – sequence: 11 givenname: Zahraa orcidid: 0000-0001-9389-2850 surname: Tarek fullname: Tarek, Zahraa
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Snippet	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...
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SubjectTerms	Accuracy Algorithms Biopsy Blood Classification Datasets Disease Feature selection Forecasts and trends Genotype & phenotype gradient boosting (GB) Hepatitis C Hepatitis C virus hepatitis C virus (HCV) hyperparameters Infection Infections Inflammation Liver cancer Liver cirrhosis Machine learning Medical research Medicine, Experimental Optimization OPTUNA
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Title	Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework
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