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Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt

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Bibliographic Details
Main Authors: Heba Mamdouh, Farghaly, Mahmoud Y., Shams, Tarek Abd, El-Hafeez
Format: Book
Language:English
Published: Springer 2023
Subjects:
Machine learning
SFS
Online Access:https://link.springer.com/article/10.1007/s10115-023-01851-4
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8260
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spelling oai:localhost:PNK-82602023-04-25T02:14:50Z Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt Heba Mamdouh, Farghaly Mahmoud Y., Shams Tarek Abd, El-Hafeez Machine learning SFS CC BY 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). 2023-04-25T02:14:50Z 2023-04-25T02:14:50Z 2023 Book https://link.springer.com/article/10.1007/s10115-023-01851-4 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8260 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Machine learning
SFS
spellingShingle Machine learning
SFS
Heba Mamdouh, Farghaly
Mahmoud Y., Shams
Tarek Abd, El-Hafeez
Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
description CC BY
format Book
author Heba Mamdouh, Farghaly
Mahmoud Y., Shams
Tarek Abd, El-Hafeez
author_facet Heba Mamdouh, Farghaly
Mahmoud Y., Shams
Tarek Abd, El-Hafeez
author_sort Heba Mamdouh, Farghaly
title Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
title_short Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
title_full Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
title_fullStr Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
title_full_unstemmed Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
title_sort hepatitis c virus prediction based on machine learning framework a real-world case study in egypt
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s10115-023-01851-4
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8260
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score 8.891787

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