Support vector machine the most fruitful algorithm for prognosticating heart disorder

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    One of the wealthiest areas of research is Data mining that is more popular in healthcare organizations. Heart disease is the main outcome of death in the human society over the recent years. Heart disease is serious life threatening diseases that result to death. In order to save a pan-tient’s life, the doctors and medical examiners are being taking many efforts. The consultant of doctor’s determination can make without the advice of specialists because of the software develop by the advancement in computer technology. In most of the papers, Data mining tech-niques used in the existing method in the research are Naive Bayes, Decision tree, J48, K-Nearest Neighbor (K-NN) (or) Lazy IBK algo-rithms to predict heart diseases. In this paper, support vector machines (SVM) technique will produce the most accuracy prediction rate for heart diseases while comparing to all the other techniques used in data mining.

     


  • Keywords


    Heart Disease; Data Mining; Decision Tree; J48; Naive Bayes; K-NN; Lazy IBK; Support Vector Machines.

  • References


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Article ID: 12533
 
DOI: 10.14419/ijet.v7i2.26.12533




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