CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Evaluation of Artificial Neural Networks in Prediction of Essential Hypertension

by Rahul Samant, Srikantha Rao
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 12
Year of Publication: 2013
Authors: Rahul Samant, Srikantha Rao
10.5120/14067-2331

Rahul Samant, Srikantha Rao . Evaluation of Artificial Neural Networks in Prediction of Essential Hypertension. International Journal of Computer Applications. 81, 12 ( November 2013), 34-38. DOI=10.5120/14067-2331

@article{ 10.5120/14067-2331,
author = { Rahul Samant, Srikantha Rao },
title = { Evaluation of Artificial Neural Networks in Prediction of Essential Hypertension },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 12 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number12/14067-2331/ },
doi = { 10.5120/14067-2331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:55.138296+05:30
%A Rahul Samant
%A Srikantha Rao
%T Evaluation of Artificial Neural Networks in Prediction of Essential Hypertension
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 12
%P 34-38
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper investigates the ability of variously designed & trained Artificial Neural Network (ANN) to predict the probability of occurrence of Hypertension (HT) in a mixed (healthy + hypertensive, both sexes) patient population. To do this a multi layer feed-forward neural network with 13 inputs and 1 output was created with multiple hidden layers. Network parameters such as count of hidden layers, count of neurons in the hidden layers, percentage of testing samples and percentage of samples used for validation were varied so as to deliver the maximum prediction accuracy of the ANN network. The training algorithm used for ANN is Levenberg-Marquardt back propagation algorithm. A large database, comprising healthy and hypertensive patients from a university hospital was used for training the ANN and prediction. The maximum accuracy marked by this approach was 92. 85%, considered quite satisfactory by medical experts. Thus the best network parameter choice best for ANNs approached empirically.

References
  1. R Dybowski and v. Gant, (2007) Clinical Applications of Artificial Neural Networks, Cambridge University Press
  2. Bishop, C. M. (1995) Neural Networks for Pattern Recognition, Oxford: Oxford University Press. ISBN 0-19-853849-9
  3. X. Zhu, (2005),Semi-supervised learning literature survey, Dept. Computer. Sci. ,Univ. Wisconsin-Madison, Madison, Tech. Rep. 1530.
  4. Rudiger W. Brause,(2000) Medical Analysis and Diagnosis by Neural Networks , URL: http://citeseerx. ist. psu. edu/viewdoc/download?doi=10. 1. 1. 1. 2901&rep=rep1&type=pdf.
  5. Lanzarini, L. , & Giusti, D. A. (2000). Pattern recognition in medical images using neural networks. IEEE Transaction on Image and Signal Processing Analysis
  6. Carretero, O. A. , & Oparil, S. (2000). Essential hypertension. Part I: Definition and etiology. Circulation, 101, 329–335.
  7. D. Gil, M. Johnsson, J. M. Garicia Chemizo, A. S. Paya and D. R. Fernandez,(2009) Application of Artificial Neural Networks in the diagnosis of Urological Dysfunctions, Expert systems with applications, Vol. 36, No. 3,2009, pp 5754-5760
  8. Mevlut Ture, Imran Kurt, A. Turhan Kurum, Kazim Ozdamar, (2005),Comparing classification techniques for predicting essential hypertension, , Expert Systems with Applications Vol. 29, pp 583–588
  9. Kuang-Hung Hsu, Chaochang Chiu, Nan-Hsing Chiu, Po-Chi Lee, Wen-Ko Chiu, Thu-Hua Liu, Chorng-Jer Hwang, (2011) A case-based classifier for hypertension detection, Knowledge-Based Systems 24 (2011) 33–39
  10. G. Zhang, P. Yan, H. Zhao and X. Zhang, (2008) A computer aided Diagnosis System in Mammography Using Artificial Neural networks , International conference on Biomedical Engineering and Informatics, Vol. 2, pp. 823-826
  11. Siddharth Jonathan, Shruthi Narayan, A Two Tier Neural inter-network based approach to Medical Diagnosis using k-Nearest Neighbor Classification for Diagnosis pruning , URL: http://infolab. stanford. edu/~jonsid/nimd. pdf
  12. H. Kordylewski. , D. Graupe, Kai Liu, (2001), A novel large-memory neural network as an aid in medical diagnosis applications, IEEE Transactions on Information Technology in Biomedicine, Sept. 2001 V 5#3.
  13. Hong Zhang, F. C. Lin, (1999), Medical diagnosis by the virtual physician, Proceedings of the 12th IEEE Symposium on Computer-Based Medical Systems, June 99, pp. 296 – 302.
  14. Danna Voth,(2005), Using AI to detect breast cancer, IEEE Intelligent Systems, Jan. /Feb. 2005 vol. 20, no. 1, pp. 5-7
  15. Qeethara Kadhim Al-Shayea, Artificial Neural Networks in Medical Diagnosis, IJCSI International Journal of Computer Science Issues, 2011, (2):150-154
  16. Rahul Samant, Srikantha Rao . " Effects of Missing Data Imputation on Classifier Accuracy ", Vol. 2 - Issue 11 (November - 2013), International Journal of Engineering Research & Technology (IJERT) , ISSN: 2278-0181 , pp 264-266
  17. Rahul Samant, Srikantha Rao . " A study on Feature Selection Methods in Medical Decision Support Systems ", Vol. 2 - Issue 11 (November - 2013), International Journal of Engineering Research & Technology (IJERT) , ISSN: 2278-0181 , pp 615-619,
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network medical diagnosis essential hypertension accuracy.

Powered by PhDFocusTM