Diagnosing the Stage of Hepatitis C Using Machine Learning

被引:12
作者
Butt, Muhammad Bilal [1 ]
Alfayad, Majed [2 ]
Saqib, Shazia [1 ]
Khan, M. A. [3 ]
Ahmad, Munir [4 ]
Khan, Muhammad Adnan [5 ]
Elmitwally, Nouh Sabri [6 ,7 ]
机构
[1] Lahore Garrison Univ, Dept Comp Sci, Lahore 54000, Pakistan
[2] Jouf Univ, Coll Comp & Informat Sci, Sakaka 72341, Saudi Arabia
[3] Riphah Int Univ, Fac Comp, Riphah Sch Comp & Innovat, Lahore Campus, Lahore 54000, Pakistan
[4] Natl Coll Business Adm & Econ, Sch Comp Sci, Lahore 54000, Pakistan
[5] Gachon Univ, Dept Software, Seongnam 13557, Gyeonggi Do, South Korea
[6] Cairo Univ, Fac Comp & Artificial Intelligence, Dept Comp Sci, Giza 12613, Egypt
[7] Birmingham City Univ, Sch Comp & Digital Technol, Birmingham B4 7XG, W Midlands, England
关键词
16;
D O I
10.1155/2021/8062410
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页数:8
相关论文
共 16 条
  • [1] The impact of artificial intelligence in medicine on the future role of the physician
    Ahuja, Abhimanyu S.
    [J]. PEERJ, 2019, 7
  • [2] Akella A., 2020, APPL MACHINE LEARNIN, V2020
  • [3] Prediction and Staging of Hepatic Fibrosis in Children with Hepatitis C Virus: A Machine Learning Approach
    Barakat, Nahla H.
    Barakat, Sana H.
    Ahmed, Nadia
    [J]. HEALTHCARE INFORMATICS RESEARCH, 2019, 25 (03) : 173 - 181
  • [4] Prediction of Fibrosis Progression Rate in Patients with Chronic Hepatitis C Genotype 4: Role of Cirrhosis Risk Score and Host Factors
    Besheer, Tarek
    El-Bendary, Mahmoud
    Elalfy, Hatem
    Abd El-Maksoud, Mohamed
    Salah, Mohamed
    Zalata, Khaled
    Elkashef, Wagdi
    Elshahawy, Heba
    Raafat, Doaa
    Elemshaty, Wafaa
    Almashad, Noha
    Zaghloul, Hosam
    El-Gilany, Abdel-Hady
    Razek, Ahmed Abdel Khalek Abdel
    Abd Elwahab, Mohamed
    [J]. JOURNAL OF INTERFERON AND CYTOKINE RESEARCH, 2017, 37 (03) : 97 - 102
  • [5] Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD
    Gawrieh, Samer
    Sethunath, Deepak
    Cummings, Oscar W.
    Kleiner, David E.
    Vuppalanchi, Raj
    Chalasani, Naga
    Tuceryan, Mihran
    [J]. ANNALS OF DIAGNOSTIC PATHOLOGY, 2020, 47
  • [6] Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients
    Hashem, Somaya
    Esmat, Gamal
    Elakel, Wafaa
    Habashy, Shahira
    Raouf, Safaa Abdel
    Elhefnawi, Mohamed
    Eladawy, Mohamed I.
    ElHefnawi, Mahmoud
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2018, 15 (03) : 861 - 868
  • [7] The practical implementation of artificial intelligence technologies in medicine
    He, Jianxing
    Baxter, Sally L.
    Xu, Jie
    Xu, Jiming
    Zhou, Xingtao
    Zhang, Kang
    [J]. NATURE MEDICINE, 2019, 25 (01) : 30 - 36
  • [8] Kamal S., 2019, UCI MACHINE LEARNING
  • [9] Mechanisms Underlying Hepatitis C Virus-Associated Hepatic Fibrosis
    Khatun, Mousumi
    Ray, Ratna B.
    [J]. CELLS, 2019, 8 (10)
  • [10] Li N., 2019, IEEE ACCESS, V7