CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Artificial Intelligence (AI) Techniques Applied for the Development of a Clinical Decision Support System (CDSS) for Diagnosing Ischemic Heart Disease (IHD)

by K. Rajeswari, V. Vaithiyanathan, Deepa Abin
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 9
Year of Publication: 2014
Authors: K. Rajeswari, V. Vaithiyanathan, Deepa Abin
10.5120/17716-8567

K. Rajeswari, V. Vaithiyanathan, Deepa Abin . Artificial Intelligence (AI) Techniques Applied for the Development of a Clinical Decision Support System (CDSS) for Diagnosing Ischemic Heart Disease (IHD). International Journal of Computer Applications. 101, 9 ( September 2014), 28-37. DOI=10.5120/17716-8567

@article{ 10.5120/17716-8567,
author = { K. Rajeswari, V. Vaithiyanathan, Deepa Abin },
title = { Artificial Intelligence (AI) Techniques Applied for the Development of a Clinical Decision Support System (CDSS) for Diagnosing Ischemic Heart Disease (IHD) },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 9 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 28-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number9/17716-8567/ },
doi = { 10.5120/17716-8567 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:14.874498+05:30
%A K. Rajeswari
%A V. Vaithiyanathan
%A Deepa Abin
%T Artificial Intelligence (AI) Techniques Applied for the Development of a Clinical Decision Support System (CDSS) for Diagnosing Ischemic Heart Disease (IHD)
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 9
%P 28-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ischemic Heart Disease (IHD) is difficult to diagnose since most of the symptoms and clinical presentations are similar to other diseases. It is a very common, harmful disease, which is identified mostly during the mortality of an individual. The objective is to build a clinical decision support system, which will diagnose the presence of IHD with an integrated automated classifier using Artificial Intelligence (AI) techniques. A retrospective data set that included 800 clinical cases was taken for the work. A total of 88 sets were discarded during pre-processing. Tests were run on 712 cases using the Weka classifiers available in Weka 3. 7. 0. Out of 113 classifiers, 16 were identified to be the best based on the following parameters: sensitivity, specificity, accuracy, F-measure, kappa statistic, correctly classified cases, time taken to run the model, and the Receiver Operating Characteristic (ROC) curve. The diagnoses made by the Clinical Decision Support System (CDSS) were compared with those made by physicians during patient consultations. The KSTAR algorithm showed the best diagnoses with the highest accuracy 97. 32%, sensitivity 98%, specificity 97% kappa 0. 95, and ROC 0. 995. The authors thus conclude that a CDSS can be developed to assist expert physicians in separating the positive and the negative cases of heart disease.

References
  1. V. Jomini, S. Oppliger-Pasquali, V. Wietlisbach, N. Rodondi, V. Jotterand, F. Paccaud, et al. "Contribution of major cardiovascular risk factors to familial premature coronary artery disease: the GENECARD project", J. Am. Coll. Cardiol. , 2002, 40, 676-684. A. Asuncion, D. J. Newman, "UCI machine learning repository", 2007. Full text available here: http://www. ics. uci. edu/~mlearn/MLRepository. html (Accessed 16th July 2012).
  2. A. Asuncion, D. J. Newman, "UCI machine learning repository", 2007. Full text available here: http://www. ics. uci. edu/~mlearn/MLRepository. html (Accessed 16th July 2012).
  3. International Society of Cardiology and the Joint Subject Group on standardization of clinical naming in World Health Organization: Naming and diagnosis criteria of ischemic heart disease. Circulation, 1979, 59(3), 607.
  4. S. Vamadevan. A. Prabhakaran, D. Prabhakaran, "Coronary heart disease in Indians: Implications of the INTERHEART study", Indian. J. Med. Res. , 2010, 132, 561-566.
  5. K. S. Reddy, B. Shah, C. Varghese, A. Ramadoss, "Responding to the threat of chronic diseases in India", Lancet, 2005, 366, 1744-9.
  6. R. Gupta, P. Joshi, V. Mohan, K. S. Reddy, S. Yusuf, "Epidemiology and causation of coronary heart disease and stroke in India", Heart, 2008, 94, 16-26.
  7. 7. S. Mendis, P. Puska, B. Norrving, "Global Atlas on Cardiovascular Disease Prevention and Control", World Health Organization, 2011, Full text available here: http://whqlibdoc. who. int/publications/2011/9789241564373_eng. pdf (accessed 10th August 2012).
  8. E. A. Enas, "Clinical Implications: Dyslipidemia in the Asian Indian Population. Dyslipidemia in the Asian Indian Population: Unique aspects and implications for treatment", American Association of Physicians of Indian Origin, Oakbrook, 2002, Full text available here: http://www. southasianheartcenter. org/docs/AAPImonograph. pdf (accessed 10th August 2012.
  9. S. O'Kelly, "Euroheart 2009", Heart Dis News, 2009, Full text available here: http://www. health. am/cardio/more/euroheart-2009/ (accessed 15th July 2012).
  10. European Society of Cardiology, "Obese adolescents have heart damage", Cardiology, 2012, Full text available here: http://medicalxpress. com/news/2012-05-obese-adolescents-heart. html (accessed 15th July 2012).
  11. J. R. Quinlan, "Induction of decision trees", Machine Learning, 1986, 1, 81-106.
  12. 12. J. R. Quinlan, "C4. 5: Programs for Machine Learning", Morgan Kaufman, San Francisco, 1993.
  13. 13. V. S. Ajay, D. Prabhakaran, P. Jeemon, K. R. Thankappan, V. Mohan, L. Ramakrishnan, et al. "Prevalence and determinants of diabetes mellitus in the Indian Industrial population", Diabetic Med, 2008, 25, 1187-1194.
  14. 14. "India's Population 2012", 2012, Full text available here: http://www. indiaonlinepages. com/population/india-current-population. html (accessed 26th July 2012).
  15. 15. Medical Council of India, Annual Report – 2009-10.
  16. 16. G. P. Liu, Y. Q. Wang, Y. Dong, et al. , "Development and evaluation of Scale for heart system Inquiry of TCM" Chin. J. Integr. Med. , 2009, 7(1), 1222-1225.
  17. E. García-Nietoa, A. L. Alba, B. E. Jourdan, B. Talbi, "Sensitivity- and specificity-based multiobjective approach for feature selection: Application to cancer diagnosis", Information Processing Letters, 2009, 109, 887-896.
  18. D. J. Newman, S. Hettich, C. L. Blake, C. J. Merz, "UCI repository of machine learning databases. Department of Information and Computer Science", University California Irvine, 1998.
  19. L. Breiman, J. Friedman, R. Olshen, C. Stone, "Classification of Regression Trees", Wadsworth, 1984.
  20. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten, "The WEKA data mining software: an update", SIGKDD Explorations, 2009, 11(1), 10-18.
  21. I. H. Witten, E. Frank, "Data Mining: Practical Machine Learning Tools and Techniques", 2nd ed. , Morgan Kaufmann, San Francisco, 2005.
  22. E. Massad, "A teoria bayesiana no diagnóstico medico", in: "Métodos quantitativos em medicina" (Ed. E. Massad, R. X. Menezes, P. S. P. Silveira, N. R. S. Ortega), Barueri (SP), Manole, 2004, 189-205.
  23. P. K. Anooj, "Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules", J. King. Saud. Univ. , – Computer and Information Sciences 2012, 24, 27-40.
  24. European ST-T Database Directory. Pisa, Italy: S. T. A. R. , 1991.
  25. M. G. Tsipouras, C. Voglis, D. I. Fotiadis, "A Framework for Fuzzy Expert System Creation-Application to Cardiovascular Diseases", IEEE Transactions on BioMedical Engineering, 2007, 54, 11.
  26. A. Gharaviri, M. Teshnehlab, H. A. Moghaddam, "Ischemia Detection via ECG Using ANFIS", Conf Proc. IEEE. Eng. Med. Biol. Soc. , 2008, 1163-1168.
  27. M. A. Karaolis, J. A. Moutiris, D. Hadjipanayi, C. S. Pattichis, "Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees", IEEE Transactions on Information Technology in BioMedicine, 2010, 14, 3.
  28. D. Frenkel, J. Nadal, "Ischemic episode detection using an artificial neural network trained with isolated ST-T segments", Comp. Cardiol. , 1999, 53-56.
  29. M. Piao, H. G. Lee, G. Y. Sohn, G. Pok, K. H. Ryo, "Emerging Patterns based Methodology for Prediction of Patients with Myocardial Ischemia", Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, 1, 174-178.
  30. National Cardiovascular Disease Database (Sticker No: SE / 04 / 233208), Supported by Ministry of Health & Family Welfare, Government of India and World Health Organization, 2011, Full text available here: http://www. whoindia. org/ (accessed 10th August 2012).
  31. P. Jeemon, K. S. Reddy, "Social determinants of cardiovascular disease outcomes in Indians", Indian. J. Med. Res,. 2010, 132, 617-622.
  32. © 2011 by Maejo University, San Sai, Chiang Mai, 50290 Thailand. Reproduction is permitted for noncommercial purposes.
Index Terms

Computer Science
Information Sciences

Keywords

Clinical Decision Support System (CDSS) Artificial Intelligence (AI) techniques Ischemic Heart Disease (IHD) Sensitivity Specificity Accuracy F-measure Kappa statistic Receiver Operating Characteristic (ROC).

Powered by PhDFocusTM