Open AccessSupport vector machine the most fruitful algorithm for prognosticating heart disorder
Author(s) -
M. Geethapriya M.Murugesan,
R Elankeerthana
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.26.12533
Subject(s) - c4.5 algorithm , naive bayes classifier , support vector machine , decision tree , machine learning , computer science , artificial intelligence , heart disease , data mining , disease , algorithm , medicine
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.