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

Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines

by G. Parthiban, A. Rajesh, S. K. Srivatsa
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 2
Year of Publication: 2012
Authors: G. Parthiban, A. Rajesh, S. K. Srivatsa
10.5120/7324-0149

G. Parthiban, A. Rajesh, S. K. Srivatsa . Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines. International Journal of Computer Applications. 48, 2 ( June 2012), 45-49. DOI=10.5120/7324-0149

@article{ 10.5120/7324-0149,
author = { G. Parthiban, A. Rajesh, S. K. Srivatsa },
title = { Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 2 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 45-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number2/7324-0149/ },
doi = { 10.5120/7324-0149 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:36.104735+05:30
%A G. Parthiban
%A A. Rajesh
%A S. K. Srivatsa
%T Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 2
%P 45-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process. Data mining techniques are used to operate on large volumes of data to discover hidden patterns and relationships helpful in decision making. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Support Vector Machines for the classification purpose. On the evidence of this we too have used SVM classifier using radial basis function kernel for our experimentation. The results of our proposed system were quite good. The system exhibited good accuracy in predicting the vulnerability of diabetic patients to heart diseases.

References
  1. J. Han Kamber, M. 2006. Data Mining: Concepts and Techniques, 2nd ed. San Francisco: Morgan Kaufman.
  2. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From Data Mining to Knowledge Discovery in Databases", AI Magazine, Vol. 17, pp. 37-54, 1996.
  3. Introduction to Data Mining and Knowledge Discovery, Third Edition ISBN: 1-892095-02-5, Two Crows Corporation 10500 Falls Road, Potomac, MD 20854 (U. S. A. ),1999.
  4. L. A. Rose, D. T. , "Discovering Knowledge in Data: An Introduction to Data Mining", ISBN O-471-66657-2, ohn Wiley & Sons, Inc, 2005.
  5. World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: Available: http://www. who. int/ diabetes/en
  6. World Health Organization. Available: http: // www. who. int/topics/ diabetes mellitus/en/
  7. International Diabetes Federation (IDF), Available: http://www. idf . org/ about-diabetes
  8. American Diabetes Association Available: http: // www. diabetes. org/
  9. NJ Morrish, Wang , Stevens , Fuller JH, Keen H. "Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes". Diabetologia 2001; 44 Suppl 2:s14 – s21 Available: http:// citeulike. org /user/mpgrayer/article/3496572
  10. R. Emslie – AM Smith Gardner ID, Morris AD. "Vascular Complications of Diabetes". British Medical Journal 2000; 320:1062 – 6 Available:http://bmj. com/BMJ 2000; 320 doi: 10. 1136/bmj. 320. 7241. 1062
  11. S. M. Haffner Lehto S, Ronnemaa T, Pyoyala K, Laakso M. "Mortality from Coronary heart disease in subjects with Type 2 Diabetes and Nondiabetic Subjects with and without prior Myocardial Infarction". The New England Journal of Medicine 1998; 339:229 – 34 Available: http:// biomedcentral. com/BMC Health Services Research 2011, 11:180 doi: 10. 1186/1472-6963-11-180
  12. Diabetes with cardiovascular disease- Available: www. idf. org/fact-sheets/diabetes-cvd
  13. Y. Huang, P. McCullagh, N. Black, and R. Harper, "Feature selection and classification model construction on type 2 diabetic patient's data," in Lecture Notes in Artificial intelligence, vol. 3275, ICDM 2004, P. Perner, Ed. Berlin: Springer-Verlag, 2004, pp. 153-162.
  14. R. Bellazzi, C. Larizza, P. Magni, S. Montani, and M. Stefanelli, "Intelligent analysis of clinical time series: an application in the diabetes mellitus domain," Artificial Intelligence in Medicine, vol. 20 pp. 37-57, 2000. Available: http://dx. doi. org/10. 1016/S0933-3657(00)00052-X
  15. R. Bellazzi, "Telemedicine and Diabetes Management: Current Challenges and Future Research Directions," Journal of Diabetes Science and Technology, vol. 2, no. 1, pp. 98-104, 2008.
  16. R. Goel, A. Misra, D. Kondal, R. M. Pandey, N. K. Vikram, J. S. Wasir, V. Dhingra, and K. Luthra, "Identification of insulin resistance in Asian Indian adolescents:classification and regression tree (CART) and logisticregression based classification rules," Clinical Endocrinology, vol. 70 pp. 717-724, 2009.
  17. K. E. Heikes, B. Arondekar, D. M. Eddy, and L. Schlessinger, "Diabetes Risk Calculator,A simple tool for detecting undiagnosed diabetes and pre-diabetes," Diabetes Care, vol. 31, no. 5, pp. 1040-1045, 2008
  18. N. Lavrac, E. Keravnou, and B. Zupan, "Intelligent Data Analysis in Medicine," in Encyclopedia of Computer Science and Technology, vol. 42, K. e. al. , Ed. New York: Dekker, 2000, pp. 113-157.
  19. W. Kong, L. Tham, K. Y. Wong, and P. Tan, "Support vector machine approach for cancer detection using amplified fragment length polymorphism (AFLP) method," Proc. the 2nd Asia-Pacific Bioinformatics Conference (APBC2004), Dunedin, New Zealand, 2004.
  20. A. Karaolis, Joseph A. Moutiris, Demetra Hadjipanayi, and Constantinos S. Pattichis. "Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees". IEEE Transaction on Information Technology in Biomedicine, Vol. 14, No. 3, May2010. Available: http://ieeexplore. ieee. org//xpls/abs_all. jsp?arnumber=5378501DigitalObject Identifier: 10. 1109/TITB. 2009. 2038906
  21. K. Srinivas, Dr. G. Raghavendra Rao, and Dr. A. Govardhan. "Analysis of Coronary Heart Disease and Prediction of Heart Attack in Coal Mining Regions Using Data Mining Techniques". The 5th International Conference on Computer Science & Education Hefei, China. August 24–27, 2010.
  22. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. "A Practical Guide to Support Vector Classification". Available: http:// www. csie . ntu. edu. tw/~cjlin.
  23. Introduction to Support Vector Machine Available: http://en. wikipedia. org/wiki/Support_vector_machine
  24. Colin Campbell and Yiming Ying, Learning with Support Vector Machines, 2011, Morgan and Claypool. Available: http://www. morganclaypool. com/doi/abs/10. 2200/S00324ED1V01Y201102AIM010?journalCode=aim
  25. H. Barakat, Andrew P. Bradley and Mohammed Nabil H. Barakat (2009) "Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus", IEEE Transactions on Information Technology in Bio Medicine, Volume 14, Issue 4, pp 1-7, 2009. Available: http://ieeexplore. ieee. org /xpls/ abs_ all. jsp?arnumber=5378519 Digital Object Identifier: 10. 1109 /TITB. 2009. 2039485
  26. N. Barakat and A. P. Bradley, "Rule Extraction from Support Vector Machines: A Sequential Covering Approach " IEEE Transactions on Knowledge and Data Engineering, Volume 19,no. 6,pp 729-741, 2007. Available: http://ieeexplore. ieee. org/stamp/stamp. jsp?arnumber=04161896 Digital Object Identifier no. 10. 1109/TKDE. 2007. 1023.
  27. S. Balakrishnan, R. Narayanaswamy, N. Savarimuthu, R. Samikannu "SVM Ranking with Backward Search for Feature Selection in Type II Diabetes Databases" 2008 IEEE International Conference on Systems, Man and Cybernetics. Available: http://ieeexplore. ieee. org /xpls /abs_all. jsp?arnumber=4811692 Digital Object Identifier: 10. 1109 /IC SMC. 2008. 4811692
  28. G. Suganya, D. Dhivya "Extracting Diagnostic rules from SVM" , Journal of Computer Applications (JCA), 2011.
  29. K. Srinivas et al. / "Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks", International Jounal on Computer Science and Engineering (IJCSE) Vol. . 02, No. 02, 2010, 250-255. Available:http://www. enggjournals. com/ijcse/doc /IJCSE10-02-02-25. pdf
  30. G. Parthiban, A. Rajesh, S. K. Srivatsa, "Diagnosis of Heart Disease for Diabetic Patients using Naïve Bayes Method", International Journal of Computer Applications (IJCA) Volume 24-No. 3, June 2011, 0975-8887. Available: http://www. ijcaonline. org/archives/volume24/number3/2933-3887 doi 10. 5120/2933-3887
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Diabetes Heart Diseases Knowledge Discovery Support Vector Machines

Powered by PhDFocusTM