Ghazal, T M, Anam, M, Hasan, M K, Hussain, M, Farooq, M S, Ammar Ali, H M, Ahmad, M and Soomro, T R (2021) Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM. Computers, Materials & Continua, 69 (1). pp. 191-203. ISSN 1546-2226
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Abstract
Hepatitis C is a contagious blood-borne infection, and it is mostly asymptomatic during the initial stages. Therefore, it is difficult to diagnose and treat patients in the early stages of infection. The disease’s progression to its last stages makes diagnosis and treatment more difficult. In this study, an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C. The dataset used for our Hep-Pred model is based on a literature study, and includes the records of 1385 patients infected with the hepatitis C virus. Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months. A former study divided the disease into four main stages. These stages have proven helpful for doctors to analyze the liver’s condition. The traditional way to check the staging is the biopsy, which is a painful and time-consuming process. This article aims to provide an effective and efficient approach to predict hepatitis C staging. For this purpose, the proposed technique uses a fine Gaussian SVM learning algorithm, providing 97.9 accurate results.
Affiliation: | Skyline University College |
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SUC Author(s): | Ghazal, T M ORCID: https://orcid.org/0000-0003-0672-7924 |
All Author(s): | Ghazal, T M, Anam, M, Hasan, M K, Hussain, M, Farooq, M S, Ammar Ali, H M, Ahmad, M and Soomro, T R |
Item Type: | Article |
Uncontrolled Keywords: | Hepatitis C; artificial intelligence; Hep-Pred; support vector machine; machine learning; hepatitis staging |
Subjects: | B Information Technology > BM Artificial Intelligence |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 02 Feb 2022 13:41 |
Last Modified: | 02 Feb 2022 13:41 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/71 |
Publisher URL: | http://www.techscience.com/cmc/v69n1/42725 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/37365 |
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