• May 03, 2016 News! IJIET Vol. 5, No. 10 has been indexed by EI (Inspec).   [Click]
  • Jun 28, 2017 News!Vol. 7, No. 9 has been indexed by Crossref.
  • Jun 22, 2017 News!Vol. 7, No. 9 issue has been published online!   [Click]
General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2012 Vol.2(3): 220-223 ISSN: 2010-3689
DOI: 10.7763/IJIET.2012.V2.114

Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients

Mai Shouman, Tim Turner, and Rob Stocker

Abstract—Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour(KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.

Index Terms—Data mining, k-nearest-neighbour, voting, heart disease

M. Shouman is with the School of Engineering and Information Technology University of New South Wales at the Australian Defence Force Academy Northcott Drive, Canberra ACT 2600. (email: m.shouman@adfa.edu.au)
T. Turner and Rob Stocker are with the School of Engineering and Information Technology University of New South Wales at the Australian Defence Force Academy Northcott Drive, Canberra ACT 2600.

[PDF]

Cite: Mai Shouman, Tim Turner, and Rob Stocker, "Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients," International Journal of Information and Education Technology vol. 2, no. 3, pp. 220-223, 2012.

Copyright © 2008-2017. International Journal of Information and Education Technology. All rights reserved.
E-mail: ijiet@ejournal.net