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Reseach Article

Decision Support System for Heart Disease Prediction using Data Mining Techniques

by Ankur Makwana, Jaymin Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 22
Year of Publication: 2015
Authors: Ankur Makwana, Jaymin Patel
10.5120/20683-3496

Ankur Makwana, Jaymin Patel . Decision Support System for Heart Disease Prediction using Data Mining Techniques. International Journal of Computer Applications. 117, 22 ( May 2015), 1-5. DOI=10.5120/20683-3496

@article{ 10.5120/20683-3496,
author = { Ankur Makwana, Jaymin Patel },
title = { Decision Support System for Heart Disease Prediction using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 22 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number22/20683-3496/ },
doi = { 10.5120/20683-3496 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:03.785277+05:30
%A Ankur Makwana
%A Jaymin Patel
%T Decision Support System for Heart Disease Prediction using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 22
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining techniques have been widely used to mine knowledgeable information from medical database. Most nations face high and expanding rates of coronary illness or Cardiovascular Disease. Despite the fact that, current pharmaceutical is creating colossal measure of information consistently, little has been carried out to utilize this accessible information to illuminate the difficulties that face a beneficial understanding of electrocardiography examination results. Computer situated in development alongside creditable Data Mining systems are utilized for proper results. Disease finding is one of the applications where Data Mining devices are demonstrating successful results. These are the main reason for death everywhere throughout the world in the past ten years. Several scientists are utilizing factual and Data Mining apparatuses to over assistance social insurance experts in the analysis of these disease. Using Hybrid Data Mining strategy in the analysis of coronary illness has been completely explored indicating satisfactory levels of accuracy.

References
  1. Chaitrali S Dangare and Sulabha S Apte. Improved study of heart disease prediction system using data mining classification techniques. International Journal of Computer Applications, 47(10):44–48, 2012.
  2. Mu-Jung Huang, Mu-Yen Chen, and Show-Chin Lee. Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Systems with Applications, 32(3):856–867, 2007.
  3. M Akhil Jabbar, BL Deekshatulu, and Priti Chandra. Heart disease classification using nearest neighbor classifier with feature subset selection. Anale. Seria Informatica, 11, 2013.
  4. Anchana Khemphila and Veera Boonjing. Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients. In Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on, pages 193–198, 2010.
  5. S Muthukaruppan and Meng Joo Er. A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Systems with Applications, 39(14):11657–11665, 2012.
  6. T Mythili, Dev Mukherji, Nikita Padalia, and Abhiram Naidu. A heart disease prediction model using svm-decision treeslogistic regression (sdl). International Journal of Computer Applications, 68(16):11–15, 2013.
  7. Jesmin Nahar, Tasadduq Imam, Kevin S Tickle, and Yi- Ping Phoebe Chen. Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4):1086–1093, 2013.
  8. Jesmin Nahar, Tasadduq Imam, Kevin S Tickle, and Yi- Ping Phoebe Chen. Computational intelligence for heart disease diagnosis: A medical knowledge driven approach. Expert Systems with Applications, 40(1):96–104, 2013.
  9. Sellappan Palaniappan and Rafiah Awang. Intelligent heart disease prediction system using data mining techniques. In Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on, pages 108–115. IEEE, 2008.
  10. Rashedur M Rahman and Fazle Rabbi Md Hasan. Using and comparing different decision tree classification techniques for mining icddr, b hospital surveillance data. Expert Systems with Applications, 38(9):11421–11436, 2011.
  11. Yuehjen E Shao, Chia-Ding Hou, and Chih-Chou Chiu. Hybrid intelligent modeling schemes for heart disease classification. Applied Soft Computing, 14:47–52, 2014.
  12. Mai Shouman, Tim Turner, and Rob Stocker. Using data mining techniques in heart disease diagnosis and treatment. In Electronics, Communications and Computers (JEC-ECC), 2012 Japan-Egypt Conference on, pages 173–177. IEEE, 2012.
  13. Yanwei Xing, Jie Wang, Zhihong Zhao, and Yonghong Gao. Combination data mining methods with new medical data to predicting outcome of coronary heart disease. In Convergence Information Technology, 2007. International Conference on, pages 868–872. IEEE, 2007.
  14. Hongmei Yan, Jun Zheng, Yingtao Jiang, Chenglin Peng, and Shouzhong Xiao. Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm. Applied soft computing, 8(2):1105–1111, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Decision Support System Health care Health records Classification