Abstract
This Paper focuses a new approach for applying association rules in the Medical Domain to discover Heart Disease Prediction. The health care industry collects huge amount of health care data which,unfortunately are not mined to discover hidden information for effective decision making.Discovery of hidden patterns and relationships often goes unexploited. Data mining techniques can help remedy this situation.Data mining have found numerous applications in Business and Scientific domains.Association rules,classification,clustering are major areas of interest in data mining. Among these,association rules have been a very active research area.In our work Genetic algorithm is used to predict more accurately the presence of Heart Disease for Andhra Pradesh Population.The main motivation for using Genetic algorithm in the discovery of high level Prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithms often used in Data Mining.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber: Data Mining Concepts and Techniques, 2nd edn. Morgan and Kaufman (2000)
Stilou, S., Bamidic, P.D., Maglareras, N., Papas, C.: Mining Association Rules from Clinical Data Bases An Intelligent Diagnostic Process in Health Care Study of Health Technology, pp. 1399–1403 (2001)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases ACM SIGMOD. In: International Conference on Management of Data, Washington, D.C. (1993)
Agrawal, Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
William, H., Hsu: Genetic Algorithms. Kansas State University (2006)
Picek, S., Golub, M.: On the Efficiency of Crossover Operators in Genetic Algorithms with Binary Representation. In: Proceedings of the 11th WSEAS International Conference on Neural Networks (2010)
Eiben, A.E., Raué, P.-E., Ruttkay, Z.: Genetic Algorithms with Multi-Parent Recombination. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 78–87. Springer, Heidelberg (1994)
Ghosh, S., et al.: Mining Frequent Item Sets using Genetic Algorithm. IJAIAÂ 1(4) (October 2010)
Youmasu, J.S.: Understanding Risk Factors For Heart Disease A Report. Oklahoma State University (2010)
Haifeng, S., et al.: The Problem of Classification in Imbalanced Data Sets. IEEE (2010)
Anandavalli: Optimized Association Rule Mining using Improved Association Rule Mining. Advance in Information Mining (2009)
Manish, Saggar., et al.: Optimizing Association Rule Mining using Improved Genetic Algorithm IEEE (2004)
Weka Tool, http://www.cs.waikato.ac.nz/ml/weka
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jabbar, M.A., Deekshatulu, B.L., Chandra, P. (2012). An Evolutionary Algorithm for Heart Disease Prediction. In: Venugopal, K.R., Patnaik, L.M. (eds) Wireless Networks and Computational Intelligence. ICIP 2012. Communications in Computer and Information Science, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31686-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-31686-9_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31685-2
Online ISBN: 978-3-642-31686-9
eBook Packages: Computer ScienceComputer Science (R0)