Diagnosing the Stage of Hepatitis C Using Machine Learning

Butt, Muhammad Bilal and Alfayad, Majed and Saqib, Shazia and Khan, M. A. and Ahmad, Manir and Khan, Muhammad Adnan and Elmitwally, Nouh (2021) Diagnosing the Stage of Hepatitis C Using Machine Learning. Journal of Healthcare Engineering, 2021. p. 8062410. ISSN 2040-2295

8062410-1.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB)


Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation.

Item Type: Article
Identification Number: https://doi.org/10.1155/2021/8062410
25 November 2021Accepted
10 December 2021Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Nouh Elmitwally
Date Deposited: 14 Dec 2021 13:57
Last Modified: 22 Mar 2023 12:00
URI: https://www.open-access.bcu.ac.uk/id/eprint/12528

Actions (login required)

View Item View Item


In this section...