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Efficient classification and analysis of ischemic heart disease using proximal support vector machines based decision trees

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3 Author(s)
Soman, K.P. ; Amrita Inst. of Manage., Amrita Univ., Coimbatore, India ; Shyam, D.M. ; Madhavdas, P.

Ischemic heart disease (IHD) is one of the toughest challenges to doctors in-making right decisions due to its skimpy symptoms and complexity. We have analyzed IHD data from 65 patients to provide an aid for decision-making. Decision trees give potent structural information about the data and thereby serve as a powerful data mining tool. Support vector machines serve as excellent classifiers and predictors and can do so with high accuracy. Our tree based classifier uses non-linear proximal support vector machines (PSVM). The accuracy is very high (100% for training data) and the tree is small and precise.

Published in:

TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region  (Volume:1 )

Date of Conference:

15-17 Oct. 2003

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