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Nhan đề : 
Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
Tác giả : 
Heba Mamdouh, Farghaly
Mahmoud Y., Shams
Tarek Abd, El-Hafeez
Năm xuất bản : 
2023
Nhà xuất bản : 
Springer
Tóm tắt : 
Prediction 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).
Mô tả: 
CC BY
URI: 
https://link.springer.com/article/10.1007/s10115-023-01851-4
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8260
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