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
Reseach Article

Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques

Published on February 2016 by Poonam Rahul Hankare, Hemalata A. Gosavi
International Conference on Advances in Science and Technology
Foundation of Computer Science USA
ICAST2015 - Number 1
February 2016
Authors: Poonam Rahul Hankare, Hemalata A. Gosavi
4d3b41c5-2727-4c10-8a34-a23a672af2e8

Poonam Rahul Hankare, Hemalata A. Gosavi . Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques. International Conference on Advances in Science and Technology. ICAST2015, 1 (February 2016), 16-19.

@article{
author = { Poonam Rahul Hankare, Hemalata A. Gosavi },
title = { Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques },
journal = { International Conference on Advances in Science and Technology },
issue_date = { February 2016 },
volume = { ICAST2015 },
number = { 1 },
month = { February },
year = { 2016 },
issn = 0975-8887,
pages = { 16-19 },
numpages = 4,
url = { /proceedings/icast2015/number1/24219-3004/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Science and Technology
%A Poonam Rahul Hankare
%A Hemalata A. Gosavi
%T Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques
%J International Conference on Advances in Science and Technology
%@ 0975-8887
%V ICAST2015
%N 1
%P 16-19
%D 2016
%I International Journal of Computer Applications
Abstract

Restorative science industry has tremendous measure of information, however shockingly the vast majority of this information is not mined to discover out shrouded data in information. Propelled information mining systems can be utilized to find shrouded design in information. Models created from these systems will be valuable for medicinal professionals to take successful choice. In this examination paper, one of the information mining arrangement system Decision Tree calculation C4. 5, ID3 and CART are dissected on cardiovascular illness dataset. Exhibitions of these calculations are thought about through affectability, specificity, exactness, blunder rate, True Positive Rate and False Positive Rate. In our studies 10-fold cross acceptance system was utilized to gauge the impartial evaluation of these expectation models. According to our outcomes, mistake rates for Decision Tree calculation C4. 5, ID3 and CART are 02. 756, 0. 2755 and 0. 2248 individually. Exactness of Decision Tree calculation C4. 5, ID3 and CART are 80. 06%, 81. 08% and 84. 12% individually. Our examination demonstrates that out of these three order method Decision Tree calculation CART predicts cardiovascular illness with minimum mistake rate and most astounding precision.

References
  1. Yanwei, X. ; Wang, J. ; Zhao, Z. ; Gao, Y. , "Combination data mining models with new medical data to predict outcome of coronary heart disease". Proceedings International Conference on Convergence Information Technology 2007, pp. 868 – 872.
  2. L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and Regression Trees, Wadsworth Int. Group, 1984.
  3. Detrano R, Steinbrunn W, Pfisterer M "International application of a new probability algorithm for the diagnosis of coronary artery disease". American Journal of Cardiology, Vol. 64, No. 3, 1987, pp. 304-310.
  4. Yao Z, Lei L, Yin J "R-C4. 5 Decision tree model and its applications to health care dataset". Proceedings of International Conference on Services Systems and Services Management 2005, pp. 1099-1103.
  5. Das R, Abdulkadir S. (2008) "Effective diagnosis of heart disease through neural networks ensembles" , Elsevier,2008.
  6. Colombet I, Ruelland A, Chatellier G, Gueyffier F 2000) "Models to predict cardiovascular risk: comparison ofCART, multilayer perceptron and logistic regression", Proceedings of AMIA Symp 2000, pp. 156-160.
  7. Quinlan J R "Induction of Decision Trees," Machine Learning. Vol. 1. 1986. 81-106.
  8. Mehmed, K. : "Data mining: Concepts, Models, Methods and Algorithms", New Jersey: John Wiley, 2003.
  9. Mohd, H. , Mohamed, S. H. S. : "Acceptance Model of Electronic Medical Record", Journal of Advancing Information and Management Studies. 2(1), 75-92, 2005.
  10. Microsoft Developer Network (MSDN). http://msdn2. microsoft. com/enus/virtuallabs/aa740409. aspx,2007.
  11. Obenshain, M. K: "Application of Data Mining Techniques to Healthcare Data", Infection Control and Hospital Epidemiology, 25(8), 690–695, 2004.
  12. Sellappan, P. , Chua, S. L. : "Model-based Healthcare Decision Support System", Proc. Of Int. Conf. on Information Technology in Asia CITA'05, 45-50, Kuching, Sarawak, Malaysia, 2005
  13. Tang, Z. H. , MacLennan, J. : "Data Mining with SQL Server 2005", Indianapolis: Wiley, 2005.
  14. Thuraisingham, B. : "A Primer for Understanding and Applying Data Mining", IT Professional, 28-31, 2000.
  15. Weiguo, F. , Wallace, L. , Rich, S. , Zhongju, Z. : "Tapping the Power of Text Mining", Communication of the ACM. 49(9), 77-82, 2006.
  16. Wu, R. , Peters, W. , Morgan, M. W. : "The Next Generation Clinical Decision Support: Linking Evidence to Best Practice", Journal Healthcare InformationManagement. 16(4), 50-55, 2002.
  17. K. SrinivasB. Kavihta Rani Dr. A. Govrdhan Associate Professor, Dept. of CSE Principal andProfessor of CSE 'Applications of Data Mining Techniques in Healthcare and Prediction of HeartAttacks '-(IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 250-255.
  18. Giudici, P. , Applied Data Mining Statistical Methods for Business and Industry. John Wiley & Sons Ltd, Chichester , England (2003).
  19. WHO. , Fact Sheet: The Top Ten Causes of Death. World Health Organization. Geneva (2006).
  20. Han, J. and Kamber, M. , Data Mining: Concepts and Techniques. Second Edition, Morgan Kaufmann Publishers, San Francisco (2006).
Index Terms

Computer Science
Information Sciences

Keywords

Active Learning Decision Support System Data Mining Medical Engineering C4. 5 Id3 And Cart.