Open Access

ARTICLE

Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

Taher M. Ghazal^1,2, Marrium Anam³, Mohammad Kamrul Hasan¹, Muzammil Hussain^4,*, Muhammad Sajid Farooq⁵, Hafiz Muhammad Ammar Ali⁴, Munir Ahmad⁶, Tariq Rahim Soomro⁷

1 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia
2 School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, UAE
3 Department of Computer Science, Government College University, Faisalabad, 38000, Pakistan
4 Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54000, Pakistan
5 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
6 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
7 CCSIS, Institute of Business Management, Karachi, 75190, Sindh, Pakistan

* Corresponding Author: Muzammil Hussain. Email:

Computers, Materials & Continua 2021, 69(1), 191-203. https://doi.org/10.32604/cmc.2021.015436

Received 21 November 2020; Accepted 26 March 2021; Issue published 04 June 2021

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.

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