CFP last date
20 January 2025
Reseach Article

Estimating the Surveillance of Liver Disorder using Classification Algorithms

by A. S. Aneeshkumar, C. Jothi Venkateswaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 6
Year of Publication: 2012
Authors: A. S. Aneeshkumar, C. Jothi Venkateswaran
10.5120/9121-3281

A. S. Aneeshkumar, C. Jothi Venkateswaran . Estimating the Surveillance of Liver Disorder using Classification Algorithms. International Journal of Computer Applications. 57, 6 ( November 2012), 39-42. DOI=10.5120/9121-3281

@article{ 10.5120/9121-3281,
author = { A. S. Aneeshkumar, C. Jothi Venkateswaran },
title = { Estimating the Surveillance of Liver Disorder using Classification Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 6 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number6/9121-3281/ },
doi = { 10.5120/9121-3281 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:46.000881+05:30
%A A. S. Aneeshkumar
%A C. Jothi Venkateswaran
%T Estimating the Surveillance of Liver Disorder using Classification Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 6
%P 39-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is an activity of extracting some useful knowledge from a large data base, by using any of its techniques. In this paper we are using classification, one of the major data mining models, which is used to predict previously unknown class of objects. Unlike other diseases, liver disorder prediction from common symptoms is typically difficult job for medical practitioners. Most of the features or symptoms are seen in many other fever related diseases and so it is not free from false assumptions. In most cases the opportunity of liver disease will not identified because of the domination of other diseases.

References
  1. Sally Jo Cunningham and Geoffrey Holmes, "Developing innovative applications in agriculture using data mining", In the Proceedings of the Southeast Asia Regional Computer Confederation Conference, 1999.
  2. Sengul DOGAN and Ibrahim TURKOGLU, "Iron-Deficiency Anemia Detection from Hematology Parameters by using Decision trees", International Lournal of Science & Technology, Volume 3, No. 1, 85-92, 2008.
  3. Asha. T, Dr. S. Natarajan and Dr. K. N. B. Murthy, "A Study of Associative Classifiers with Different Rule Evaluation Measures for Tuberculosis Prediction", IJCA Special Issue on "Artificial Intelligence Techniques-Novel Approaches & Practical Applications", AIT, 2011
  4. P. Rajeswari and G. Sophia Reena, "Analysis of Liver Disorder Using Data mining Algorith", Global Journal of Computer Science and Technology, vol. 10 issue 14(ver. 1. 0) November 2010.
  5. Shantakumar B. Patil and Y. S. Kumaraswamy, "Intelligent and effective heart attack Prediction System using Data mining and Artificial neural network", European journal of Scientific research, ISSN 1450-216X Vol. 31 No. 4(2009), pp. 642-656.
  6. Jiawei Han and Micheline Kamber, "Data Mining Concepts and Techniques", Published by Elsevier, second edition – 2006.
  7. V. Ramesh and K. Ramar, "Classification of Agricultural Lands Soil: A Data Mining Approach", Agricultural Journal 6(3): 82-86, 2011, ISSN: 1816-9155.
  8. Bishop, C. M. (1996). "Neural networks for Pattern Recognition", Clarendon Press, Oxford.
  9. Huda Yasin, Tahseen A. Jilani and Madiha Danish, "Hepatitis-C Classification using Data Mining Techniques", International Journal of Computer Applications (0975-8887), Volume 24-No. 3, June 2011.
  10. Polat K. and Gunes S. , "Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation", Digital Signal Processing 16, 2006, pp. 889-901.
  11. Das S K, Mukherjee S, Vasudevan D M and Balakrishnan V, "Comparison of haematological parameters in patients with non-alcoholic fatty liver disease and alcoholic liver disease", Singapore Med J 2011: 52(3): 175.
  12. K. Rajeswari, Dr. V. Vaithiyanathan and 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.
  13. "Heart disease burden in the next two years", http://www. medicalnewstoday. com / articles / 105302 . php.
  14. A. Sudha, P. Gayathri and N. Jaisankar, "Effective Analysis and Predictive Model of Stroke Disease using Classification Methods", International Journal of Computer Applications (0975-8887) Volume 43-No. 14, April 2012.
  15. Peiman Mamani Barnaghi, Vahid Alizadeh Sahzabi and Azuraliza Abu Bakar, "A Comparative Study for Various Methods of Classification", 2012 International Conference on Information and Computer Networks, IPCSIT vol. 27 (2012) @ IACSIT Press, Singapore. .
  16. "India's no. 1 killer: Heart disease", http://indiatoday. intoday. in/story/ India's+no. 1 +killer:+ Heart= disease/ 1/92422. html.
  17. Torky I. Sultan, Ayman Khedr and Samir Sabry, "Biochemical Markers of Fibrosis for Chronic Liver Disease: Data mining-based Approach", International Journal of Engineering Research and Development, Volume 2, issue 2 (July 2012), PP. 08-15.
Index Terms

Computer Science
Information Sciences

Keywords

Preprocessing Naive Bayesian C4. 5 decision tree