An Evolutionary Algorithm for Heart Disease Prediction

  • M. A. Jabbar
  • B. L. Deekshatulu
  • Priti Chandra
Part of the Communications in Computer and Information Science book series (CCIS, volume 292)


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.


Andhra Pradesh Association Rules Evolutionary Computation Heart Disease 


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  1. 1.
    Han, J., Kamber: Data Mining Concepts and Techniques, 2nd edn. Morgan and Kaufman (2000)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    William, H., Hsu: Genetic Algorithms. Kansas State University (2006)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Ghosh, S., et al.: Mining Frequent Item Sets using Genetic Algorithm. IJAIA 1(4) (October 2010)Google Scholar
  9. 9.
    Youmasu, J.S.: Understanding Risk Factors For Heart Disease A Report. Oklahoma State University (2010)Google Scholar
  10. 10.
    Haifeng, S., et al.: The Problem of Classification in Imbalanced Data Sets. IEEE (2010)Google Scholar
  11. 11.
    Anandavalli: Optimized Association Rule Mining using Improved Association Rule Mining. Advance in Information Mining (2009)Google Scholar
  12. 12.
    Manish, Saggar., et al.: Optimizing Association Rule Mining using Improved Genetic Algorithm IEEE (2004)Google Scholar
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • M. A. Jabbar
    • 1
  • B. L. Deekshatulu
    • 2
  • Priti Chandra
    • 3
  1. 1.JNTUHyderabadIndia
  2. 2.IDRBT, RBI Government of IndiaIndia
  3. 3.Advanced System LaboratoryHyderabadIndia

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