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A Web Based Cardiovascular Disease Detection System

  • Patient Facing Systems
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Abstract

Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD 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 decision tree classification algorithm. The algorithm has been 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.

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References

  1. ABI-Med: website: http://www.abi-med.com/. Accessed on December 2014

  2. Anbarasi, M., Anupriya, E., and Iyengar, N. C. S. N., Enhanced prediction of heart disease with feature subset selection using genetic algorithm. Int. J. Eng. Sci. Technol. 2.10:5370–5376, 2010.

    Google Scholar 

  3. http://www.bem.fi/book/19/19.htm

  4. 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. Accessed on December 2014

  5. Dangare, C. S., and Dr Sulabha S. A., A data mining approach for prediction of heart disease using neural networks. Int. J. Comput. Eng. Technol. 3.3:30–40, 2012.

    Google Scholar 

  6. Dharminder, K., and Bhardwaj, D., Rise of Data Mining: Current and Future Application Areas. IJCSI Int. J. Comput. Sci. Issues 8, 2011.

  7. WEKA data mining software. Accessed on December (2014)

  8. Global status report on noncommunicable diseases 2010: Geneva, World Health Organization (2011)

  9. Global atlas on cardiovascular disease prevention and control. Geneva, World Health Organization (2011)

  10. http://gpete-neil.blogspot.com/2011/02/simulating-complex-ecg-patterns-with.html

  11. Hong, J., Kim, S., and Zhang, B., AptaCDSS-E: A classifier ensemble based clinical decision support system for cardiovascular disease level prediction. Expert Syst. Appl. 34:1, 2008.

    Article  Google Scholar 

  12. Jones, S. A.: ECG Notes Interpretation and Management Guide (2005)

  13. PhysioBank Archive Index, Physionet, Cambridge. Website: http://www.physionet.org/physiobank/database. Accessed on December 2014

  14. Vishwa, A., et al.: Clasification of arrhythmic ECG data using machine learning techniques. 67-70 (2011)

  15. Prasad, G. K., and Sahambi, J. S., Classification of ECG arrhythmias using multi-resolution analysis and neural networks. IEEE 1, 2003.

  16. Farid, M., and Yakoub, B., Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE,667–677, 2008.

  17. Stanislaw, O., Hoai, L. T., and Markiewicz, T.: Support vector machine-based expert system for reliable heartbeat recognition. IEEE, 582–589 (2004)

  18. Nasiri, J. A., Naghibzadeh, M., Sadoghi Yazdi, H., and Naghibzadeh, B.: ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm, IEEE, 187–192 (2009)

  19. Ceylan, R., Yuksel O., and Bekir, K., A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Syst. Appl. 36. 3:6721–6726, 2009.

    Article  Google Scholar 

  20. Ozbay, Y., Ceylan, R., and Karlik, B., A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Comput. Biol. Med. 36.4:376–388, 2006.

    Article  Google Scholar 

  21. UCI Machine Learning Repository. Website: http://archive.ics.uci.edu/ml/datasets/Arrhythmia. Accessed on December 2014

  22. Yan, H., et al., A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Syst. App. 30.2:272–281, 2006.

    Article  Google Scholar 

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Correspondence to Hussam Alshraideh.

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This article is part of the Topical Collection on UCAmI & IWAAL 2014

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Alshraideh, H., Otoom, M., Al-Araida, A. et al. A Web Based Cardiovascular Disease Detection System. J Med Syst 39, 122 (2015). https://doi.org/10.1007/s10916-015-0290-7

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  • DOI: https://doi.org/10.1007/s10916-015-0290-7

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