Hepatitis C Prediction Using Machine Learning and Deep Learning-Based Hybrid Approach with Biomarker and Clinical Data-Reference-Cited by-同舟云学术

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Hepatitis C Prediction Using Machine Learning and Deep Learning-Based Hybrid Approach with Biomarker and Clinical Data

Published:2024-06-05 Issue: Volume: Page:
ISSN:2731-4812
Container-title:Biomedical Materials & Devices
language:en
Short-container-title:Biomedical Materials & Devices

Author:

Ripa Rokiya,Uddin Khandaker Mohammad Mohi^ORCID,Alam Mir Jafikul,Rahman Md. Mahbubur

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s44174-024-00197-x.pdf

Reference54 articles.

1. O.O. Oladimeji, A. Oladimeji, O. Olayanju, Machine learning models for diagnostic classification of hepatitis C tests. Front. Health Inform. 10(1), 70 (2021)

2. J.S. Skyler, G.L. Bakris, E. Bonifacio, T. Darsow, R.H. Eckel, L. Groop, P. Groop, Y. Handelsman, R.E. Ratner, Differentiation of diabetes by pathophysiology. Nat. Hist. Progn. Diabetes 66(2), 241–255 (2017)

3. Z. Tao, A. Shi, J. Zhao, Epidemiological perspectives of diabetes. Cell Biochem. Biophys. 73, 181–185 (2015)

4. World Health Organization. Hepatitis C2020 [cited: 9 Nov 2020]. Available from: www.who.int/news-room/fact-sheets/detail/hepatitis-c

5. Centers for Disease Control and Prevention. Hepatitis. 2018 [cited: 9 Nov 2020]. Available from: www.cdc.gov/hepatitis/hcv/cfaq.htm

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