Citation

Albogamy, Fahad R. and Asghar, Junaid and Subhan, Fazli and Asghar, Muhammad Zubair and Al-Rakhami, Mabrook S. and Khan, Aurangzeb and Mohamad Nasir, Haidawati and Rahmat, Mohd Khairil and Alam, Muhammad Mansoor and Lajis, Adidah and Mohd Su'ud, Mazliham (2022) Decision Support System for Predicting Survivability of Hepatitis Patients. Frontiers in Public Health, 10. ISSN 2296-2565

Text fpubh-10-862497.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Viral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence data.

Item Type:	Article
Uncontrolled Keywords:	binary classification
Subjects:	Q Science > QB Astronomy
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Oct 2022 01:49
Last Modified:	06 Oct 2022 01:50
URII:	http://shdl.mmu.edu.my/id/eprint/10221

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