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dc.contributor.authorHeba Mamdouh, Farghaly-
dc.contributor.authorMahmoud Y., Shams-
dc.contributor.authorTarek Abd, El-Hafeez-
dc.date.accessioned2023-04-25T02:14:50Z-
dc.date.available2023-04-25T02:14:50Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1007/s10115-023-01851-4-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/8260-
dc.descriptionCC BYvi
dc.description.abstractPrediction and classification of diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine learning (ML) approaches are commonly used for predicting and classifying diseases that are precisely utilized as an efficient tool for doctors and specialists. This paper proposes a prediction framework based on ML approaches to predict Hepatitis C Virus among healthcare workers in Egypt. We utilized real-world data from the National Liver Institute, founded at Menoufiya University (Menoufiya, Egypt). The collected dataset consists of 859 patients with 12 different features. To ensure the robustness and reliability of the proposed framework, we performed two scenarios: the first without feature selection and the second after the features are selected based on sequential forward selection (SFS).vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjectMachine learningvi
dc.subjectSFSvi
dc.titleHepatitis C Virus prediction based on machine learning framework a real-world case study in Egyptvi
dc.typeBookvi
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