Skip to main content
SpringerLink
Log in

Improving the Probability of Clinical Diagnosis of Coronary-Artery Disease Using Extended Kalman Filters with Radial Basis Function Network

  • Conference paper
  • First Online:
Wireless Mobile Communication and Healthcare (MobiHealth 2016)

  • 1286 Accesses

Abstract

Kalman filters have been popular in applications to predict time-series data analysis and prediction. This paper uses a form of Extended Kalman Filter to predict the occurrence of CAD (Coronary Artery Disease) using patients data based on different relevant parameters. The work takes a novel approach by using different neural networks training algorithms Quasi-Newton and SCG with combination of activation functions to predict the existence/non-existence of CAD in a patient based on patient’s data set. The prediction probability of this combination is resulted in accuracy of about 92% or above, using cross validation and thresholding to remove the limitation of time-series prediction introduced because of the Extended Kalman filter behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. WHO: WHO World Health Organization, January 2015. http://www.who.int/mediacentre/factsheets/fs317/en/. Accessed 6 Mar 2016

  2. NIH: What is Coronary Heart Disease, National Institute of Health, 3 October 2015. http://www.nhlbi.nih.gov/health/health-topics/topics/cad. Accessed 6 Mar 2016

  3. British Heart Foundation: Risk factors (2015). https://www.bhf.org.uk/heart-health/risk-factors. Accessed 6 Mar 2016

  4. Wan, E.A., Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: The IEEE Conference on Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000, AS-SPCC (2000)

    Google Scholar 

  5. Julier, S.J., Uhlmann, J.K.: New extension of the Kalman filter to nonlinear systems. In: AeroSense 1997 International Society for Optics and Photonics (1997)

    Google Scholar 

  6. Genders, T.S.S., Steyerberg, E.W., Hunink, M.G.M., Nieman, K.: Prediction model to estimate presence of coronary artery disease: Retrospective pooled analysis of existing cohorts. Br. Med. J. 344, 1–13 (2012)

    Article  Google Scholar 

  7. Yamada, H., Do, D., Morise, A., Atwood, J.E., Froeliche, V.: Review of studies using multivariable analysis of clinical and exercise test data to predict angiographic coronary artery disease. Prog. Cardiovasc. Dis. 39, 457–481 (1997)

    Article  Google Scholar 

  8. Pryor, D.B., Harrell, F.E., Lee, K.L., Califf, R.M., Rosati, R.A.: Estimating the likelihood of significant coronary artery disease. Am. J. Med. 75, 771–780 (1983)

    Article  Google Scholar 

  9. Diamond, G.A., Forrester, J.S.: Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. N. Engl. J. Med. 300, 1350–1358 (1979)

    Article  Google Scholar 

  10. Kalman, R.: A new approach to linear filtering and prediction problems. J. Basic Eng. 83, 35–45 (1960). Transaction of the ASME

    Article  Google Scholar 

  11. Heydari, S.T., Ayatollahi, S.M.T., Zare, N.: Comparison of artificial neural networks with logistic regression for detection of obesity. J. Med. Syst. 36(4), 2449–2454 (2012)

    Article  Google Scholar 

  12. Hongzong, S., Tao, W., Xiaojun, Y., Huanxiang, L., Zhide, H., Mancang, L., BoTao, F.: Support vector machines classification for discriminating coronary heart disease patients from non-coronary heart disease. West Indian Med. J. 56(5), 451–457 (2007)

    Google Scholar 

  13. Hedeshi, N.G., Abadeh, M.S.: Coronary artery disease detection using a fuzzy-boosting PSO approach. Comput. Intell. Neurosci 6 (2014)

    Google Scholar 

  14. Karabulut, E.M., İbrikçi, T.: Effective diagnosis of coronary artery disease using the rotation forest ensemble method. J. Med. Syst. 36, 3011–3018 (2012)

    Article  Google Scholar 

  15. Oh, S.: Matrix Completion: Fundamental Limits and Efficient Algorithms. Stanford University, California (2010)

    Google Scholar 

  16. Wu, T.T., Lange, K.: Matrix completion discriminant analysis. Comput. Stat. Data Anal. 92, 115–125 (2015)

    Article  MathSciNet  Google Scholar 

  17. Arif, M., Malagore, I.A., Afsar, F.A.: Detection and localization of myocardial infarction using K-nearest neighbor classifier. J. Med. Syst. 36, 279–289 (2010)

    Article  Google Scholar 

  18. Comak, E.: A biomedical decision support system using LS-SVM classifier with an efficient and new parameter regularization procedure for diagnosis of heart valve diseases. J. Med. Syst. 36, 549–556 (2010)

    Article  Google Scholar 

  19. Salamah, M.: The Statistic analysis Study of Coronary-Artery Disease Data Based on King Abdullah Medical City in Saudi Arabia (KAMC-CAD). Coventry (2016)

    Google Scholar 

  20. Haykin, S.: Kalman Filtering and Neural Networks. Wiley, New York (2001)

    Book  Google Scholar 

  21. Sepasi, S., Ghorbani, R., Liaw, B.Y.: Improved extended kalman filter for state of charge estimation of battery pack. J. Power Sources 255, 368–376 (2014)

    Article  Google Scholar 

  22. Sun, X., Jin, L., Xiong, M.: Extended Kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks. PLoS ONE 3(11), 1–13 (2008)

    Google Scholar 

Download references

Acknowledgment

We are very thankful to King Abdullah Medical City in Saudi Arabia for providing the patient data to be used in this study.

Author information

Authors and Affiliations

  1. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, CV1 5FB, UK

    Mashail Alsalamah & Saad Amin

Authors
  1. Mashail Alsalamah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saad Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mashail Alsalamah .

Editor information

Editors and Affiliations

  1. Polytechnic University of Milan, Milan, Italy

    Paolo Perego

  2. Polytechnic University of Milan, Milan, Italy

    Giuseppe Andreoni

  3. Institute of Molecular Bioimaging and Physiology, Segrate, Italy

    Giovanna Rizzo

Rights and permissions

Reprints and permissions

Copyright information

© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Alsalamah, M., Amin, S. (2017). Improving the Probability of Clinical Diagnosis of Coronary-Artery Disease Using Extended Kalman Filters with Radial Basis Function Network. In: Perego, P., Andreoni, G., Rizzo, G. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-319-58877-3_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-58877-3_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58876-6

  • Online ISBN: 978-3-319-58877-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation