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
Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43533, Egypt.
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.
Diagnostics (Basel). 2023 Nov 13;13(22):3439. doi: 10.3390/diagnostics13223439.
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.
本文聚焦于埃及的丙型肝炎病毒（HCV）感染情况，该国是世界上HCV感染率最高的国家之一。高感染率与多种因素有关，包括注射吸毒、医疗机构消毒措施不力以及公众意识淡薄。本文介绍了一种hyOPTGB模型，该模型采用优化的梯度提升（GB）分类器来预测埃及的HCV疾病。通过使用OPTUNA框架优化超参数提高了模型的准确性。采用最小-最大归一化作为预处理步骤来缩放数据集值，并使用前向选择（FS）包装方法来识别关键特征。该研究中使用的数据集包含1385个实例和29个特征，可从UCI机器学习库获取。作者将包括决策树（DT）、支持向量机（SVM）、虚拟分类器（DC）、岭分类器（RC）和装袋分类器（BC）在内的五种机器学习模型的性能与hyOPTGB模型进行了比较。使用包括准确率、召回率、精确率和F1分数在内的各种指标评估了该系统的有效性。hyOPTGB模型的表现优于其他机器学习模型，准确率达到了95.3%。作者还将hyOPTGB模型与使用相同数据集的其他作者提出的模型进行了比较。
