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A Web Based Cardiovascular Disease Detection System

  • Hussam Alshraideh
  • Mwaffaq Otoom
  • Aseel Al-Araida
  • Haneen Bawaneh
  • José Bravo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8867)

Abstract

Nowadays, Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening common health issues. Early detection of CVD is one of the most important solutions to reduce its devastating effects on health. In this paper, an efficient detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29% accuracy for the algorithm. The algorithm is also integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient’s body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application.

Keywords

Cardiovascular Classification Electrocardiography WEKA 

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References

  1. 1.
  2. 2.
  3. 3.
    Data Mining Community’s Top Resource, “Industries where you applied Analytics/Data Mining in 2011”. Website: http://www.kdnuggets.com/polls/2011/industries-applied-anaytics-data-mining
  4. 4.
    Kumar, D., Bhardwaj, D.: Rise of Data Mining: Current and Future Application Areas. IJCSI International Journal of Computer Science Issues 8 (September 2011)Google Scholar
  5. 5.
  6. 6.
    Global status report on noncommunicable 20iseases 2010. Geneva, World Health Organization (2011)Google Scholar
  7. 7.
    Global atlas on cardiovascular disease prevention and control. Geneva, World Health Organization (2011)Google Scholar
  8. 8.
  9. 9.
    Jones, S.A.: ECG Notes Interpretation and Management Guide (2005)Google Scholar
  10. 10.
    PhysioBank Archive Index, Physionet, Cambridge. Website: http://www.physionet.org/physiobank/database
  11. 11.
    Vishwa, A., et al.: Classification of arrhythmic ECG data using machine learning techniques, pp. 67–70 (2011)Google Scholar
  12. 12.
    Prasad, G.K., Sahambi, J.S.: Classification of ECG arrhythmias using multi-resolution analysis and neural networks, vol. 1. IEEE (2003)Google Scholar
  13. 13.
    Melgani, F., Bazi, Y.: Classification of electrocardiogram signals with support vector machines and particle swarm optimization, pp. 667–677. IEEE (2008)Google Scholar
  14. 14.
    Osowski, S., Hoai, L.T., Markiewicz, T.: Support vector machine-based expert system for reliable heartbeat recognition, pp. 582–589. IEEE (2004)Google Scholar
  15. 15.
    Nasiri, J.A., Naghibzadeh, M., Sadoghi Yazdi, H., Naghibzadeh, B.: ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm, pp. 187–192. IEEE (2009)Google Scholar
  16. 16.
    Ceylan, R., Ozbay, Y., Karlik, B.: A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Systems with Applications 36(3), 6721–6726 (2009)CrossRefGoogle Scholar
  17. 17.
    Ozbay, Y., Ceylan, R., Karlik, B.: A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Computers in Biology and Medicine 36(4), 376–388 (2006)CrossRefGoogle Scholar
  18. 18.
    UCI Machine Learning Repository. Website: http://archive.ics.uci.edu/ml/datasets/Arrhythmia

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hussam Alshraideh
    • 1
  • Mwaffaq Otoom
    • 2
  • Aseel Al-Araida
    • 1
  • Haneen Bawaneh
    • 1
  • José Bravo
    • 3
  1. 1.Jordan University of Science and TechnologyJordan
  2. 2.Yarmouk UniversityJordan
  3. 3.Castilla-La Mancha UniversitySpain

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