CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Analysis of Associative Classification for Prediction of HCV Response to Treatment

by Enas M. F. El Houby
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 15
Year of Publication: 2013
Authors: Enas M. F. El Houby
10.5120/10545-5542

Enas M. F. El Houby . Analysis of Associative Classification for Prediction of HCV Response to Treatment. International Journal of Computer Applications. 63, 15 ( February 2013), 38-44. DOI=10.5120/10545-5542

@article{ 10.5120/10545-5542,
author = { Enas M. F. El Houby },
title = { Analysis of Associative Classification for Prediction of HCV Response to Treatment },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 15 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number15/10545-5542/ },
doi = { 10.5120/10545-5542 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:26.496099+05:30
%A Enas M. F. El Houby
%T Analysis of Associative Classification for Prediction of HCV Response to Treatment
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 15
%P 38-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this research is the analysis of predicting the response for treatment in patient with hepatitis C virus. The Interferon Alfa (IFN) in combination with ribavirin (RBV) is used as a standard therapy for chronic hepatitis C (CHC), it is very expensive and accompanied with great side effects, with that it fails in more than half cases. For the prediction of treatment response, a knowledge discovery framework includes two main phases: pre-processing and data mining was developed. In pre-processing phase, the clean and selection of suitable features from patients' data were done. In data mining phase the selected patients' features were mined using Associative Classification (AC) technique to generate a set of Class Association Rules (CARs). The most suitable rules from the generated CARs were selected to build a classifier, which predicts patients' response for treatment. Using our model, 220 patients treated with IFN plus RBV were analyzed, 92 patients resulted responders and 128 non-responders at the end of treatment and during the follow up. 170 cases had been used to train our intelligent systems and 50 patients had been used to test the model. The experiment results showed that the proposed technique is an effective classification technique with high prediction accuracy reach up to 90%.

References
  1. Seeff, L. B. , "Natural history of chronic hepatitis C", Hepatology, 36, 2002: S35-S46.
  2. Maiellaro, P. A. , et al. , "Artificial Neural Networks for the Prediction of Response to Interferon Plus Ribavirin Treatment in Patients with Chronic Hepatitis C", Current Pharmaceutical Design, Vol. 10, No. 17, 2004, pp. 2101-2109.
  3. McHutchison JC, et al. "Predicting response to initial therapy with interferon plus ribavirin in chronic hepatitis C using serum HCV RNA results during therapy", J Viral Hepat 2001; 8: 414-20.
  4. McHutchison JG, Hoofnagle JH, 2000. "Therapy of Chronic Hepatitis C", In: Liang TJ, Hoofnagle JH eds. Hepatitis C. Biomedical Research Reports. San Diego (CA): Academic Press 2000; 203-239.
  5. Poynard T, et al. , "Randomised trial of interferon alpha2b plus ribavirin for 48 weeks or for 24 weeks versus interferon alpha2b plus placebo for 48 weeks for treatment of chronic infection with hepatitis C virus", International Hepatitis Interventional Therapy Group (IHIT). Lancet 1998; 352: 1426-32.
  6. Rattanakronkul, N. and K. Waiyamai,"Combining Association Rule Discovery and Data Classification for Protein Structure Prediction", The International Conference on Bio-informatics (INCOP'2002).
  7. Thabath, F. ,"A review of associative classification mining", Knowledge Engineering Review, 22(1),2007, 37-65.
  8. Thabtah, F. A. and P. I. Cowling, "A greedy classification algorithm based on association rule", Applied Soft Computing, 7(3), 2006, 1102-1111.
  9. Szymon, W. ,"Towards prediction of HCV therapy efficiency", Computational and Mathematical Methods in Medicine, 11(2) 2010, 185-199.
  10. Asselah, T. et al. , "Hepatitis C: viral and host factors associated with non-response to pegylated interferon plus ribavirin", Liver International, 30, 2010, pp. 1259-1269.
  11. Berenguer M. , et al. , "A Model to Predict Severe HCV-Related Disease Following Live Transplantation", HEPATOLOGY, Vol. 38, No. 1, 2003, PP. 34-41.
  12. Moucari R. , et al, "High predictive value of early viral kinetics in retreatment with peginterferon and ribavirin of chronic hepatitis C patients non-responders to standard combination therapy", Journal of Hepatology 46, 2007, PP. 596–604.
  13. Wang, D. et al. , "A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy," Artificial Intelligence in Medicine, 47, 2009, 63-74.
  14. Lau-Corona, D. , et al. , "Effective use of fibro test to generate decision trees in hepatitis C," Journal of Gastroenterology, 15, 2009, pp. 2617-2622.
  15. Kurosaki, M. et al. , "A predictive model of response to peg interferon ribavirin in chronic hepatitis C using classification and regression tree analysis", Hepatology Research, 40, 2010, pp 251-260.
  16. Hassan, M. et al. , "The Decision tree Mode for Prediction the Response to the Treatment in Patients with Chronic Hepatitis C", New York Science Journal, 4(7), 2011, pp. 69-79.
  17. El-Houby E. M. F. , "Mining Protein Structure Class Using One Database Scan", International Journal of the Computer, the Internet and Management (IJCIM), 18(2), 2010, pp 8-16.
  18. ElHefnawi, M. , et al. , "Prediction of prognostic biomarkers for Interferon-based therapy to Hepatitis C Virus patients: a metaanalysis of the NS5A protein in subtypes 1a, 1b, and 3a", Virology Journal 2010, 7: 130.
  19. El-Houby E. M. F. , Hassan M. S. , "Using Associative Classification for Treatment Response Prediction", Journal of Applied Sciences Research, 8(10): 5089-5095, 2012, ISSN 1819-544X.
  20. Floares A. G. , Alexandru George Floares, "Artificial Intelligence Support for Interferon Treatment Decision in Chronic Hepatitis B", World Academy of Science, Engineering and Technology, vol. 44, 2008, pp. 110-115.
  21. Liu, B. , et al. , "Integrating Classification and Association Rule Mining", the proceedings of the International1998 Int. Conf. KDD'98. New York, USA, pp: 80-86.
Index Terms

Computer Science
Information Sciences

Keywords

Associative classification Class association rules hepatitis C virus interferon ribavirin

Powered by PhDFocusTM