Machine Learning Techniques on Liver Disease - A Survey

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    A Liver infection causing Chronic Hepatitis B virus affects around 257 million people around the globe. About one million people who are infected from chronic infections like HBV die from chronic liver disease. Along these lines, there is a solid requirement for effective, exact and practical framework to foresee the result of such infection. It will be helpful in taking precaution steps and proper treatment. Machine learning assumes an essential job in restorative industry. Experts in machine learning today can guarantee an accurate and definite diagnosis and analysis of disease.  Machine learning methodologies have been approached on various liver disease related datasets to predict outcome result. Machine learning calculations are exceptionally useful in giving essential measurements, continuous information, and progressed examination regarding the patients' illness, "lab test results, circulatory strain, family history, clinical preliminary information, and more to" specialists. Motivation behind this paper is to give an overview on relative survey on machine learning techniques that has been used on various liver disease datasets.

     

     


  • Keywords


    Liver Disease; Linear Regression; Support Vector Machines; Decision Tree; Random Forest; Ensemble Models.

  • References


      [1] D.A. Saleh F. Shebl M. Abdel-Hamid etal. “Incidence and risk factors for hepatitis C infection in a cohort of women in rural Egypt” Trans. R. Soc. Trop. Med.Hyg, vol. 102 pp. 921928, 2008.

      [2] S. A. Gonzalez dan E. B. Keeffe, “Acute liver failure,” dalam Handbook of Liver Disease Third Edition, Philadelphia, Elsevier, 2012, pp. 20-33

      [3] M. Priya et al, “Performance Analysis of Liver disease prediction using Machine Learning Algorithms”, IRJET e-ISSN: 2395-0056 Vol. 05, Issue: 1, Jan 2018.

      [4] N. Li, Y. Jiang, G. Gong, G. Han and J. Ma, "Non-Invasive Assessment Model of Liver Disease Severity by Serum Markers Using Cloud Computing and Internet of Things," in IEEE Access, vol. 6, pp. 33969-33976, 2018.

      [5] Dr.S.Vijayarani, Mr.S.Dhayanand, "Liver Disease Prediction using SVM and Naïve Bayes Algorithms",International Journal of Science, Engineering and Technology Research(IJSETR), 2015.

      [6] Sumedh Sontakke, J. L. (20l7). "Diagnosis of Liver Diseases using Machine Learning." ICEI.

      [7] “A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis “ Bendi Venkata Ramana, Prof. M.Surendra Prasad Babu, Prof. N. B. Venkateswarlu -( IJDMS ), Vol.3, No.2,May 2012

      [8] “Data Mining Techniques for optimization of liver disease classification.” Sadiyah Noor Novita Alfisahrin, Teddy Mantoro Electronic ISBN: 978-1-4799-2758-6 DOI:

      [9] 10.1109/ACSAT.2013.81-IEEE

      [10] “Prediction of Liver Fibrosis stages by Machine Learning model: A Decision Tree Approach*” Heba Ayeldeen,, Olfat Shaker , Ghada Ayeldeen , Khaled M. Anwar, Electronic ISBN: 978-1-4673-9669-1IEEE-2016 https://doi.org/10.1109/ICoCS.2015.7483212.

      [11] H. Wang, Y. Liu and W. Huang, "Random forest and Bayesian prediction for Hepatitis B virus reactivation," 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, 2017, pp. 2060-2064.

      [12] M. R. Haque, M. M. Islam, H. Iqbal, M. S. Reza and M. K. Hasan, "Performance Evaluation of Random Forests and Artificial Neural Networks for the Classification of Liver Disorder," 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, 2018, pp. 1-5.doi: 10.1109/IC4ME2.2018.8465658

      [13] M. Ramasamy, S. Selvaraj and M. Mayilvaganan, "An empirical analysis of decision tree algorithms: Modeling hepatitis data," 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2015, pp. 1-4. doi:

      [14] 10.1109/ICETECH.2015.7275013

      [15] Bendi Venkata Ramanaland Prof.M.Surendra Prasad Babu, “Liver Classification Using ModifiedRotation Forest”, Internationa Journal of Engineering Research and Development ISSN: 2278-067X,Volume 1, Issue6, (June 2012), PP.17-24.

      [16] Bashir S., Qamar U., Khan F. H., Naseem L. HMV: a medical decision support framework using multi-layer classifiers for disease prediction. Journal of Computational Science. 2016;13:10–25. doi: 10.1016/j.jocs.2016.01.001. [CrossRef]

      [17] Al-Shayea, Qeethara. (2011). Artificial Neural Networks in Medical Diagnosis. Int J Comput Sci Issues. 8. 150-154.

      [18] D. Xu, H. Fu and W. Jiang, "Research on Liver Disease Diagnosis Based on RS_LMBP Neural Network," 2016 12th International Conference on Computational Intelligence and Security (CIS), Wuxi, 2016, pp. 646-649.doi: 10.1109/CIS.2016.0156

      [19] G. S. Uttreshwar and A. A. Ghatol, "Hepatitis B Diagnosis Using Logical Inference And Generalized Regression Neural Networks," 2009 IEEE International Advance Computing Conference, Patiala, 2009, pp. 1587-1595.doi: 10.1109/IADCC.2009.4809255


 

View

Download

Article ID: 23207
 
DOI: 10.14419/ijet.v7i4.19.23207




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.