Research Article
Intelligent Diagnosis of Heart Diseases using Neural Network Approach
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 1 - Issue 2 |
| Published: February 2010 |
| Authors: Ranjana Raut, S. V. Dudul |
10.5120/31-140
|
Ranjana Raut, S. V. Dudul . Intelligent Diagnosis of Heart Diseases using Neural Network Approach. International Journal of Computer Applications. 1, 2 (February 2010), 97-102. DOI=10.5120/31-140
@article{ 10.5120/31-140,
author = { Ranjana Raut,S. V. Dudul },
title = { Intelligent Diagnosis of Heart Diseases using Neural Network Approach },
journal = { International Journal of Computer Applications },
year = { 2010 },
volume = { 1 },
number = { 2 },
pages = { 97-102 },
doi = { 10.5120/31-140 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2010
%A Ranjana Raut
%A S. V. Dudul
%T Intelligent Diagnosis of Heart Diseases using Neural Network Approach%T
%J International Journal of Computer Applications
%V 1
%N 2
%P 97-102
%R 10.5120/31-140
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Experiments with the Switzerland Heart Disease database have concentrated on attempting to distinguish presence and absence. The classifiers based on various neural networks, namely, MLP, PCA, Jordan, GFF, Modular, RBF, SOFM, SVM NNs and conventional statistical techniques such as DA and CART are optimally designed, thoroughly examined and performance measures are compared in this study. With chosen optimal parameters of MLP NN, when it is trained and tested over cross validation (unseen data sets), the average (and best respectively) classification of 98±2.83 % (and 100%), 96.67±4.56% overall accuracy, sensitivity 96±5.48, specificity 100% are achieved which shows consistent performance than other NN and statistical models. The results obtained in this work show the potentiality of the MLP NN approach for heart diseases classification.
References
- Bonow, Libby, Mann, Zipes, 2006. Heart Disease: a textbook of cardiovascular medicine, eighth edition, Saunders, Elsevier.
- Mathers C. D., Lopez A., Stein D. 2004. Deaths and disease burden by cause: Global burden of disease estimates by World Bank Country Groups.
- Principe J., Euliano, N., Lefebvre, C. 1999. "Neural and adaptive systems: fundamentals through simulations", John Wiley and Sons.
- Murphy, P. M. and Aha, D. W. 2004. UCI Machine Learning Databases Repository Irvine C. A. : University of California, Department of Information and Computer science, ftp://ftp.ics.uci.edu/pub/machine-learning-databases/heart/
- Haykin, S. 2007. "Neural network: A Comprehensive foundation", Pearson Prentice Hall, New Delhi.
- Bose, N.K. and Liang, P. 2001. "Neural network fundamentals with graphs, algorithms, and applications", Tata McGraw- Hill publishing company Ltd. New Delhi.
- Tokan Fikret , Türker Nurhan and Yildirim Tülay, 2006. "ROC Analysis as a Useful Tool for Performance Evaluation of Artificial Neural Networks" Lecture Notes in Computer Science, Publisher Springer Berlin / Heidelberg, ISSN 0302-9743 (Print) 1611-3349 (Online), Volume 4132/2006, Book Artificial Neural Networks - ICANN.
- Bishop C. 1997. "Neural Networks for pattern Recognition", Oxford University Press, New York.
- Lippmann, R.P. 1987. "An introduction to computing with neural nets", IEEE ASSP Magazine, pp.4-22.
- Hagan, M.T., Demuth H.B., Beale M.H. 1997. "Neural Network Design, PWS publishing", Boston, MA.
- Neurosolution version 5.07, 2007. Neuro Dimension, Inc. Gainesville, Florida, (USA),
- MATLAB: Neural Networks Toolbox users guide, The Math Works, Inc., Natick, MA, 2008.
Index Terms
Keywords