CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

The Prediction of Prevalence and Spread of HIV/AIDS using Artificial Neural Network – The Case of Rivers State in the Niger Delta, Nigeria

by Barileé Baridam, Chukwuma Irozuru
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 2
Year of Publication: 2012
Authors: Barileé Baridam, Chukwuma Irozuru
10.5120/6239-8584

Barileé Baridam, Chukwuma Irozuru . The Prediction of Prevalence and Spread of HIV/AIDS using Artificial Neural Network – The Case of Rivers State in the Niger Delta, Nigeria. International Journal of Computer Applications. 44, 2 ( April 2012), 42-45. DOI=10.5120/6239-8584

@article{ 10.5120/6239-8584,
author = { Barileé Baridam, Chukwuma Irozuru },
title = { The Prediction of Prevalence and Spread of HIV/AIDS using Artificial Neural Network – The Case of Rivers State in the Niger Delta, Nigeria },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 2 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number2/6239-8584/ },
doi = { 10.5120/6239-8584 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:31.939290+05:30
%A Barileé Baridam
%A Chukwuma Irozuru
%T The Prediction of Prevalence and Spread of HIV/AIDS using Artificial Neural Network – The Case of Rivers State in the Niger Delta, Nigeria
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 2
%P 42-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

HIV/AIDS has gained popularity and sufficient research time in the last two centuries. Research has shown that it is most predominant in people between the ages of 15-50. A lot of government and nongovernment organizations have been actively involved in finding ways to help monitor and curb the spread of the disease. Hitherto, there is no clear relevant predictive service available to HIV/AIDS control and research agencies. In this paper, the artificial neural network (ANN) is used in the prediction of prevalence and spread of HIV/AIDS. Results from a detailed analysis of a sample data used prove the robustness of the method.

References
  1. The World Bank, Building on Evidence: A Situational Analysis of the HIV Epidemic and Policy Response in Honduras, The Global HIV/AIDS Program. Available at: http://siteresources. worldbank. org/INTHIVAIDS/Resources/375798-1103037153392/HondurasSynthesisSummary. pdf
  2. Buvé, T. and Timothy. C, 2011 HIV/AIDS prevalence and behaviour in drug users and pregnant women in Kashgar Prefecture: Case report. Harm Reduction Journal 3:7, 3-7
  3. Mathew, N. 2011 Nigeria Progress Report. Pages 20-25
  4. Le C. T. and Boen J. R. 1995 Health and numbers: basic biostatistical methods. John Wiley, Chichester.
  5. HIV/AIDS Estimates 2008, HIV & Hepatitis Epidemiology Program, Spring Quarterly Report: April
  6. Ghys, P. D. , Walker N. , McFarland W, Miller R, Garnett G. P. 2008 Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections Sexually Transmitted Infections , Volume 84, pp i1-i4, doi:10. 1136/sti. 2008. 032573
  7. Ghys, P. D. , Walker N. , Garnett G. P. 2006: Improving analysis of the size and dynamics of AIDS epidemics Sexually Transmitted Infections, Volume 82, pp iii1-iii2 doi:10. 1136/sti. 2006. 021030
  8. Ghys, P. D. , Brown, T. , Grassly N. C. , Garnett G, Stanecki K. A. , Stover J. , Walker N. 2004 The UNAIDS Estimation and Projection Package: a software package to estimate and project national HIV epidemics, Sexually Transmitted Infections, Volume 80 (Suppl I), pp i5–i9. doi: 10. 1136/sti. 2004. 010199
  9. Lippincott Williams and Wilkins 2011: HIV in hiding: methods and data requirements for the estimation of the number of people living with undiagnosed HIV, Working Group on Estimation of HIV Prevalence in Europe, AIDS, Volume 25, pages 1017–1023
  10. Methods and assumptions for estimates, UNAIDS Report, Available at http://www. unaids. org/en/media/unaids/contentassets/documents/dataanalysis/Epi_MethodsAssumptionsEstimates_en. pdf, visited on February 26, 2012
  11. Natalia Ladnaya 2008 Epidemiologic Models Introduction to Work Book, Russian Federal AIDS Centre, ASAP Regional Training on Strategic and Operational Planning in HIV & AIDS, September 29 -October 10, Budapest, Hungary
  12. Owolabi, O. and Baridam, B. B. 2011 Predicting Mortality in Hepatitis-C Patients Using an Artificial Neural Network. Global Journal of Pure and Applied Science, Volume 17, No. 2, Pages 159-164,
  13. Patterson, D, 1996. Artificial Neural Networks. Prentice Hall, Singapore
  14. Fröhlich, J. , 1997. Neural networks with java. http://pages. cpsc. ucalgary. ca/~carman/533/progress/progress_report. doc
  15. Haykin, S. , 1994. Neural Networks: A Comprehensive Foundation. : Macmillan Publishing, New York.
  16. Wilbert Sibanda and Philip Pretorius 2011 Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics International Journal of Computer Applications (0975 – 8887) Volume 35, No. 5, Pages 26-31, December.
  17. Fausett, L. , 1994. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, Inc.
  18. McCullough, P. and J. A. Nelder, 1989. Generalized Linear Models, 2nd ed. , London: Chapman & Hall.
  19. Alexander, I. and H. Morton, 1995. An Introduction to Neural Computing (Second Edition), Thomson Computer Press.
  20. Anderson, J. A. , 1995. An Introduction to Neural Networks. Cambridge, MA/Bradford Books.
  21. McClelland, J. L. and D. E. Rumelhart, (Eds. ). 1988. Explorations in parallel distributed processing: A handbook of models, programs, and exercises. Cambridge, MA: MIT Press.
  22. M. Cengiz Çolak, Cemil Çolak, Hasan Kocatürk, Seref Sagiroglu, Irfan Barutçu 2008 Predicting coronary artery disease using different artificial neural network models, CAD and Artificial Neural Network, Volume 8, pages 249-254,
  23. Muhammad Akmal Sapon, Khadijah Ismail and Suehazlyn Zainudin 2011. Prediction of Diabetes by using Artificial Neural Network, International Conference on Circuits, System and Simulation IPCSIT vol. 7, pp 299-303
  24. Mantzaris,D. H. , Anastassopoulos, G. C. , Lymberopoulos, D. K. 2008 Medical disease prediction using Artificial Neural Networks, 8th IEEE International Conference on Bioinformatics and Bioengineering, pp. 1-6.
  25. Uttreshwar, G. S. ; Ghatol, A. A. 2009 Hepatitis B Diagnosis Using Logical Inference and Generalized Regression Neural Networks, IEEE International Advance Computing Conference, pp. 1587-1595.
  26. Rivers State, http://en. wikipedia. org/wiki/Rivers_State
  27. George Onah 2011: Rivers' LG records 4,746 HIV/AIDS positive cases, Vanguard News, October 5, , Available at: http://www. vanguardngr. com/2011/10/rivers-lg-records-4746-hivaids-positive-cases/
  28. H. A. A. Ugboma, M. E. Kooffreh and C. A. Nwauche 2011 Gender differences in student's knowledge of HIV/AIDS in the Niger Delta, Nigeria Journal of Clinical Medicine and Research Vol. 3(1), pp. 20-22
Index Terms

Computer Science
Information Sciences

Keywords

Hiv/aids Prediction Artificial Neural Network

Powered by PhDFocusTM