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Development of a Data Clustering Algorithm for Predicting Heart

by Bala Sundar V, T Devi, N Saravanan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 7
Year of Publication: 2012
Authors: Bala Sundar V, T Devi, N Saravanan
10.5120/7358-0095

Bala Sundar V, T Devi, N Saravanan . Development of a Data Clustering Algorithm for Predicting Heart. International Journal of Computer Applications. 48, 7 ( June 2012), 8-13. DOI=10.5120/7358-0095

@article{ 10.5120/7358-0095,
author = { Bala Sundar V, T Devi, N Saravanan },
title = { Development of a Data Clustering Algorithm for Predicting Heart },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 7 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number7/7358-0095/ },
doi = { 10.5120/7358-0095 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:26.854834+05:30
%A Bala Sundar V
%A T Devi
%A N Saravanan
%T Development of a Data Clustering Algorithm for Predicting Heart
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 7
%P 8-13
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research paper proposes the findings of the accuracy of the result by using the K-Means clustering technique in prediction of heart disease diagnosis with real and artificial datasets. K-Means Clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Each cluster is assigned a random target number of clusters-k and started from a random initialization. The proposed technique classifies the group of the objects based on attributes into K number of groups. The grouping is done by minimizing the sum of squares of distances between data using Euclidean distance formula and the corresponding cluster centroid. The research result shows that the integration of clustering gives promising results with highest accuracy rate and robustness.

References
  1. Alexander Rakhlin, Andrea Caponnetto, ?Stability of K-Means Clustering?, 2006.
  2. Arun K. Pujari, ?Data Mining Techniques?, Universities Press (India) Ltd, 2001.
  3. Blake, C. L. , Mertz, C. J. ?UCI Machine Learning Databases?, 2004.
  4. Cipolla, Emil T. ?Data Mining: Techniques to Gain Insight into Your Data Enterprise Systems Journal?, pp. 18-24, 64 December, 1995.
  5. Clifton, Christopher, "Encyclopedia Britannica: Definition of Data Mining", 2010.
  6. Duda RO, Hart PE, Stork DG. ?Pattern Classification?. New York: Wiley-Interscience, 2000
  7. Fayyad, Piatetsky-Shapiro, Smyth, "From Data Mining to Knowledge Discovery: An Overview", in, Advances in Knowledge Discovery and Data Mining, AAAI Press / The MIT Press, Men, 1996.
  8. Gerhard Münz, Sa Li, and Georg Carle, "Traffic anomaly detection using k-means clustering", In Proc. of performance, reliability and dependability evaluation of communication networks and distributed systems, 4 GI / ITG Workshop MMBnet 2007, Hamburg, Germany, September 2007.
  9. Guthrie L, Walker E, Guthrie J. Document classification by machine: theory and practice. Proceedings of the 15th International Conference on Computational Linguistics: Association for Computational Linguistics, Morristown, NJ. pp. 1059-1063, 1994.
  10. Harleen Kaur,Siri Krishan Wasan,?Empirical Study on Applications of Data Mining Techniques in Healthcare, Journal of Computer Science 2 (2): 194-200, ISSN 1549-3636, 2006.
  11. Jain A. K, Murthy M. N and Flynn P. J. Data Clustering: A Review, ACM Computing Reviews, November 1999.
  12. Jyoti Soni, Ujma Ansari, Dipesh Sharma, ?Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction?, International Journal of Computer Applications (0975 – 8887) Volume 17– No. 8, March 2011.
  13. Kavitha K. S, K. V. Ramakrishnan, 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
  14. Khaled Hammouda, Prof. Fakhreddine Karray ?A Comparative Study of Data Clustering Techniques?. University of Waterloo, Ontario, Canada, Volume 13, Issues 2-3, pp. 149-159, November 1997.
  15. Latha Parthiban and R. Subramanian, ? Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm?, International Journal of Biological and Life Sciences 3:3 2007.
  16. Meira Jr. , Zaki, M. Fundamentals of Data Mining Algorithms, 2009.
  17. Miller, A. , Blott, B. , & Hames, T. ? Review of Neural Network Applications in Medical Imaging and Signal Processing?. Medical and Biological Engineering and Computing, 30(5), pp: 449-464, 1992.
  18. Rennie JDM, Shih L, Teevan J, Karger Dr. Tackling the poor assumptions of Naive Bayes text classifiers. Proceedings of the Twentieth International Conference on Machine Learning pp. 616-23, 2003.
  19. Sellappan Palaniappan, Rafiah Awang,. ? Intelligent Heart Disease Prediction System Using Data Mining Techniques. Computer Systems and Applications, 2008. AICCSA 2008. IEEE / ACS International Conference on, pp. 108-115, March 31-April 4 2008.
  20. Shantakumar, B. Patil and Y. S Kumaraswamy. , ?Intelligent and Effective Heart attack Prediction System Using Data Mining and Artificial Neural Network, Eurp Journals Publishing Inc. ISSN 1450-216X Vol. 31 No. 4 2009, pp. 642-656, 2009.
  21. Shawkat Ali A B M,Saleh A. Wasimi, ?Data Mining : Methods and Techniques ,Cengage Learning Indis Ltd , 2009.
  22. Shearer C. ?The CRISP-DM model: the new blueprint for data mining, Data Warehousing Vol. 5, pp. 13-22, 2000.
  23. Siri Krishan Wasan, Vasudha Bhatnagar and Harleen Kaur, ?The Impact of Data mining techniques on Medical Diagnostics?, Data Science Journal, Volume 5, 19, October 2006.
  24. Srinivas K, B. Kavihta Rani and Dr. A. Govrdhan. , ?Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks, International Journal on Computer Science and Engineering,Vol. 02, No. 02,pp. 250-255, 2010.
  25. Subbalakshmi Mrs. G, E. Anupriya, N. CH. S. N. Iyengar, ?Enhanced Prediction of Heart Disease with feature Subset Selection using Genetic Algorithm, International Journal of Engineering Science and Technology Vol. 2(10), pp. 5370-5376, 2010.
  26. Tahseen A. Jilani, Huda Yasin, Madiha Yasin, Cemal Ardil, ?Acute Coronary Syndrome Prediction Using Data Mining Techniques-an Application? International Journal of Information and Mathematical Sciences 5:4, 2009
  27. Tapas Kanungo, David M. Mount, Nathan. An Efficient k-Means Clustering Algorithm: Analysis and Implementation IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, July 2002
  28. Mitchell Tom M, ?Machine Learning, Singapore, McGraw- Hill. 1997.
  29. Weinstein, J. N. , Kohn, K. W. Neural computing in Cancer Drug Development: Predicting Mechanisms of Action. Science. pp. 447-451, 1992.
  30. Wynne Hsu, Mong-Li Lee, Bing Liu, Tok Wang Ling, ?Exploration Mining in Diabetic Patients Databases: Findings and Conclusions, KDD 2000: pp: 430-436, 2000.
  31. http://cbcl. mit. edu/publications/ps/rakhlin-stability-clustering. pdf. K-Means Clustering. 05-03-11.
  32. http://mlearn. ics. uci. edu/databases/heartdisease/,. Heart Disease Dataset. 09-09-10.
  33. http://www. anderson. ucla. edu/faculty/jason. frand/teacher/technologies/palace/datamining. htm. Data mining techniques. 07-12-10.
  34. http://www. britannica. com/EBchecked/topic/1056150 /data-mining. Data Mining. 19-11-10.
  35. http://www2. cs. uregina. ca/~dbd/cs831/notes/kdd/1_kdd. html. Data mining. 18-10-10.
  36. Zakaria Nouir, Berna Sayrac, Benoît Fourestié, Walid Tabbara, and Françoise Brouaye, "Generalization Capabilities Enhancement of a Learning System by Fuzzy Space Clustering," Journal of Communications, Vol. 2, No. 6, pp. 30-37, November 2007.
  37. F. H. Saad, B. de la Iglesia, and G. D. Bell, ? A Comparison of Two Document Clustering Approaches for Clustering Medical Documents, Proceedings of the 2006 International Conference on Data Mining (DMIN-06), 2006.
  38. C. Ordonez, ? Programming the K-Means Clustering Algorithm in SQL, Proc. ACM Int'l Conf. Knowledge Discovery and Data Mining, pp. 823-828, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree Naive Bayes Neural Network K-means Clustering

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