Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework
Ahmed M. Elshewey,
Mahmoud Y. Shams,
Sayed M. Tawfeek,
Amal H. Alharbi,
Abdelhameed Ibrahim,
Abdelaziz A. Abdelhamid,
Marwa M. Eid,
Nima Khodadadi,
Laith Abualigah,
Doaa Sami Khafaga,
Zahraa Tarek
Affiliations
Ahmed M. Elshewey
Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43533, Egypt
Mahmoud Y. Shams
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Sayed M. Tawfeek
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Amal H. Alharbi
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdelhameed Ibrahim
Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Abdelaziz A. Abdelhamid
Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Marwa M. Eid
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Nima Khodadadi
Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
Laith Abualigah
Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
Doaa Sami Khafaga
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Zahraa Tarek
Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt
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.
