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

Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks

by Vanisree K, Jyothi Singaraju
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 6
Year of Publication: 2011
Authors: Vanisree K, Jyothi Singaraju
10.5120/2368-3115

Vanisree K, Jyothi Singaraju . Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks. International Journal of Computer Applications. 19, 6 ( April 2011), 6-12. DOI=10.5120/2368-3115

@article{ 10.5120/2368-3115,
author = { Vanisree K, Jyothi Singaraju },
title = { Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 6 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number6/2368-3115/ },
doi = { 10.5120/2368-3115 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:15.927437+05:30
%A Vanisree K
%A Jyothi Singaraju
%T Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 6
%P 6-12
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Congenital Heart Disease is one of the major causes of deaths in children. However, a proper diagnosis at an early stage can result in significant life saving. Unfortunately, all the physicians are not equally skilled, which can cause for time delay, inaccuracy of the diagnosis. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. In the present paper, a Decision Support System has been proposed for diagnosis of Congenital Heart Disease. The proposed system is designed and developed by using MATLAB’s GUI feature with the implementation of Backpropagation Neural Network. The Backpropagation Neural Network used in this study is a multi layered Feed Forward Neural Network, which is trained by a supervised Delta Learning Rule. The dataset used in this study are the signs, symptoms and the results of physical evaluation of a patient. The proposed system achieved an accuracy of 90%.

References
  1. Anitha Saxena, “Congenital Heart Disease in India: a Status Report”, Indian Journal of Pediatrics, Vol. 72, pp. 595-598. July 2005.
  2. Hongmei Yan, Yingtao Jiang, Jun Zheng, Chengtin Pemg, Qinghui Li, “A multi layer perceptron based Medical Decision Support System for heart disease diagnosis”, Expert System with Applications Vol. 30, pp. 272-281, Oct 2006.
  3. Lucila Ohno-Machoda, Medical applications of Artificial Neural Networks : Connectionist Models of Survival, Ph.D dissertation, Dept of Medical Information Sciences, Stanford University ,March-2006.
  4. Shantakumar B. Patil, Y.S. Kumaraswamy, “Intelligent and Effective Heart Attack Prediction System using Data Mining and Artificial Neural Networks”, European Journal of Scientific Research, ISSN 1450-216X, Vol. 31, No. 4, pp. 642-656, 2009.
  5. Hongmei, Yan, Jun Zheng, “Development of a decision Support System for Heart disease Diagnosis using Multi Layer Perceptron”, ISCAS Proceedings, Vol. 5, pp. 25-28 , 2003.
  6. Soumya Sakha Tripathy, System for Diagnosing Valvular Heart Disease using Heart Sounds, M.Tech thesis, Dept.of Computer Science and Technology, Indian Institute of Technology, Kanpur, Jan-2005.
  7. Niti Guru, Anil Dhahiya, Navin Rajpal, Decision Support System for Heart Disease Diagnosis using Neural Network, Delhi Business Review, Vol. 8, No. 1, Jan-Jun2008.
  8. Nasser Nafaa Khamins, Amir Fared Partu, “Heart Disease Diagnosis based on Multichannel Adaptive Resonance Theory”, Asian Journal of Information Technology, ISSN:1682-3919, Vol. 8, No. 2, pp. 37-46, 2009.
  9. M Esposito and G Della Vecchia, “Congeinital Heart Disease: An Ontology – based approach for the examination of the cardiovascular system”, Springer Link Publications, ISSN 0302-9743, Vol. 5177, pp. 509-516, 2008.
  10. Congenital Heart Disease Index, Available at http://www.medicinenet.com/congenital_heart_disease/
  11. Congenital heart defects in children, Available at http://www.mayoclinic.com/health/congenital-heart-defects/DS01117
  12. Congenital Heart Disease: Symptoms & Signs, available: http://www.healthopedia.com/congenital-heart-disease/symptoms/
  13. Congenital Heart Defects, available at http://bioportfolio.com/resource/pmarticle/14093/congenital-heart-defects/
  14. Michael E.Rimer, “Improving Neural Network Classification Training”, Ph.D dissertation, submitted to Dept. of Computer Science, Brigham young University, December-2007.
  15. Laurene Fausett, “Fundamental of Neural Networks” , 3rd Edition, Pearson Education.
  16. S Rajasekaran, G A Vijayalakshmi Pai, “Neural Networks, Fuzzy logic, and Genetic Algorithms synthesis and Applications”, Eastern Economy Edition.
  17. S N Sivanandam, S Sumathi, S N Deepa, “Introduction to Neural Networks using Matlab: a computer engineering series”, McGraw-Hill.
Index Terms

Computer Science
Information Sciences

Keywords

Congenital Heart Disease Disease Diagnosis Decision Support System Backpropagation Neural Network

Powered by PhDFocusTM