CFP last date
20 December 2024
Reseach Article

An Approach of Data Mining for Predicting the Chances of Liver Disease in Ectopic Pregnant Groups

Published on February 2013 by A. S. Aneeshkumar, C. Jothi Venkateswaran
International Conference on Communication, Computing and Information Technology
Foundation of Computer Science USA
ICCCMIT - Number 2
February 2013
Authors: A. S. Aneeshkumar, C. Jothi Venkateswaran
014e42e4-0fb5-4143-aad3-aee2ccc64063

A. S. Aneeshkumar, C. Jothi Venkateswaran . An Approach of Data Mining for Predicting the Chances of Liver Disease in Ectopic Pregnant Groups. International Conference on Communication, Computing and Information Technology. ICCCMIT, 2 (February 2013), 19-22.

@article{
author = { A. S. Aneeshkumar, C. Jothi Venkateswaran },
title = { An Approach of Data Mining for Predicting the Chances of Liver Disease in Ectopic Pregnant Groups },
journal = { International Conference on Communication, Computing and Information Technology },
issue_date = { February 2013 },
volume = { ICCCMIT },
number = { 2 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 19-22 },
numpages = 4,
url = { /specialissues/icccmit/number2/10332-1017/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Communication, Computing and Information Technology
%A A. S. Aneeshkumar
%A C. Jothi Venkateswaran
%T An Approach of Data Mining for Predicting the Chances of Liver Disease in Ectopic Pregnant Groups
%J International Conference on Communication, Computing and Information Technology
%@ 0975-8887
%V ICCCMIT
%N 2
%P 19-22
%D 2013
%I International Journal of Computer Applications
Abstract

Diseases are the most serious social and expensive problem faced by the society. In the past decade, world has experienced a rapid increase in various Liver diseases and Ectopic Pregnancy. In this work we propose a novel approach to evaluate the increased tendency of ectopic pregnancy and liver disease among such groups, using data mining techniques. It's due to the modern adaptive life style and cultural changes of our society.

References
  1. Huda Yasin, Tahseen A. Jilani and Madiha Danish, "Hepatitis-C Classification using Data Mining Techniques", International Journal of Computer Applications (975-8887), Volume 24- No. 3, June 2011.
  2. Michael Morelli and Anthony J. DeSimone Jr. "Application of Dempster-Shafer Theory of Evidence to the Correlation Problem" ISFI@ 2002.
  3. P. Rajeswari, G. Sophia Reena, "Analysis of Liver Disorder Using Data mining Algorithm", Global Journal of Computer Science and Technology, vol. 10 issue 14(ver. 1. 0) November 2010.
  4. Polat. K and Gunes. S, "An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease", Digital Signal Processing, 17(4), 2007, pp. 702-710.
  5. Asha. T, Dr. S. Natarajan, Dr. K. N. B. Murthy, "A Data Mining Approach to the Diagnosis of Tuberculosis by Cascading Clustering and Classification", AIT, 2011
  6. Hosmer D. and Lemeshow S. , "Applied Logistic Regression", John Wiley and Sons, 2nd edition, 2000.
  7. Ahmed Mohamed samir ali gamal eldin, Egypt, "(IJACSA) International Journal of Advanced Computer Science and Applications", Vol 2, No. 12, Dec 2011.
  8. Sudheep Elayidom. M, Sumam Mary Idikkula and Joseph Alexander, "Applying Statistical Dependency Analysis Techniques In a Data Mining Domain", International Journal Of Data Engineering (IJDE), Volume (1): Issue (2).
  9. K. Rajeswari, Dr. V. Vaithiyanathan, Dr. P. Amirtharaj, Prediction of risk score for heart disease in India using machine Intelligence, 2011 International Conference on Information and Network Technology, IPCSIT vol. 4(2011) IACSIT Press, Singapore.
  10. Sunita Soni, O. P. Vyas, Using Associative classifiers for Predictive Analysis in Health care Mining, International Journal of Computer Applications(0975-8887) Vol4-No. 5, July 2010.
  11. Anne-Louise M Heath, Cynthia Reeves Tuttle, Megan SLSimons and Christine L Cleghorn, "Longitudinal study of diet and iron deficiency anaemia in infants during the first two years of life", Asia Pacific J Clin Nutr (2002) 11(4): 251–257.
  12. R Zuzarte, C C Khong, "Recurrent ectopic pregnancy following ipsilateral partial salpingectomy", Singapore Med J 2005; 46(9) : 476.
  13. http://www. drmalpani. com/book/chapter19. html
  14. Yun-Hsuen Lim, Soon P. Ng, Paul H. O. Ng, Ay E. Tan and Muhammad A. Jamil, "Laparoscopic salpingectomy in tubal pregnancy: Prospective randomized trial using endoloop versus electrocautery", J. Obstet. Gynaecol. Res. Vol. 33, No. 6: 855–862, December 2007.
  15. Hamidah Jantan, Abdul Razak Hamdan, and Zulaiha Ali Othman, "Knowledge Discovery Techniques for Talent Forecasting in Human Resource Application", World Academy of Science, Engineering and Technology 50 2009.
  16. K. S. Kavitha , K. V. Ramakrishnan and Manoj Kumar Singh , "Modeling and design of evolutionary neural network for heart disease detection", IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010, ISSN (Online): 1694-0814.
  17. Dasgupta, D. and F. Gonzalez, "An immunity-based technique to characterizeintrusions in computer networks", IEEE Trans. Evol. Comput. 6 (3), 1081-1088, June 2002.
  18. Leinbach SS, Bhat RA, Xia SM, Hum WT, Stauffer B, Davis AR, Hung PP, Mizutani S, "Substrate specificity of the NS3 series proteinase of hepatitis C virus as determined by mutagenesis at the S3/NS4A junction", Virology 1994, 204:163-169.
  19. Portnoy, L. , E. Eskin, and S. J. Stolfo, "Intrusion detection with unlabeled data using clustering", In Proc. of ACM CSSWorkshop on Data Mining Applied to Security (DMSA-2001), Philadelphia. ACM, 5-8 November, 2001.
  20. G. Florez, SM. Bridges, Vaughn RB, "An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection", Annual Meeting of The North AmericanFuzzy Information Processing Society Proceedings, 2002.
  21. Lee, W. , S. J. Stolfo, and K. W. Mok, " Mining in a data-flow environment: Experience in network intrusion detection," In S. Chaudhuri and D. Madigan (Eds. ), Proc. of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99), San Diego, CA, pp. 114124. ACM,12-15 August 1999.
  22. Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, Published by Elsevier, second edition – 2006.
  23. http://en. wikipedia. org/wiki/Ectopic_pregnancy
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Regression Analysis Hypothesis