Prediction of Heart Disease Using 2-Tier SVM Data Mining Algorithm

AUTHORS

Priyamwada Sharma,School of Information Technology, RGPV, India

ABSTRACT

Data Mining is a field which extensively used in medical industry for extracting the useful information of various diseases. Heart disease is the major problem which occurs most frequently in human being. During the prediction of heart disease huge amounts of data generated which are too complex and voluminous to be processed and analyzed by traditional methods.By using data mining methodology it takes less time for the prediction of the disease with more accuracy. In this paper, proposes a hybrid SVM and KNN which can effectively diagnose and extract useful information from the outsized dataset. The simulation tool MATLAB is used for experimental analysis between existing methods and propose method using measuring parameter sensitivity, specificity and accuracy. The simulation of propose method gives better results than and can effectively diagnose the problem of heart disease.

 

KEYWORDS

Heart Disease, Data Mining, Medical Industry, SVM, KNN, MATLAB

REFERENCES

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CITATION

  • APA:
    Sharma,P.(2017). Prediction of Heart Disease Using 2-Tier SVM Data Mining Algorithm. International Journal of Advanced Research in Big Data Management System, 1(1), 11-24. 10.21742/IJARBMS.2017.1.1.02
  • Harvard:
    Sharma,P.(2017). "Prediction of Heart Disease Using 2-Tier SVM Data Mining Algorithm". International Journal of Advanced Research in Big Data Management System, 1(1), pp.11-24. doi:10.21742/IJARBMS.2017.1.1.02
  • IEEE:
    [1] P.Sharma, "Prediction of Heart Disease Using 2-Tier SVM Data Mining Algorithm". International Journal of Advanced Research in Big Data Management System, vol.1, no.1, pp.11-24, Jun. 2017
  • MLA:
    Sharma Priyamwada. "Prediction of Heart Disease Using 2-Tier SVM Data Mining Algorithm". International Journal of Advanced Research in Big Data Management System, vol.1, no.1, Jun. 2017, pp.11-24, doi:10.21742/IJARBMS.2017.1.1.02

ISSUE INFO

  • Volume 1, No. 1, 2017
  • ISSN(p):2208-1674
  • ISSN(e):2208-1682
  • Published:Jun. 2017

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