Submit Manuscript

Trends in Applied Sciences Research

Year: 2009 | Volume: 4 | Issue: 1 | Page No.: 36-46
Research Article

Artificial Neural Network as a Clinical Decision-Supporting Tool to Predict Cardiovascular Disease

Beatrice Fidele
Faculty of Science, University of Mauritius, Réduit, Mauritius
Jayrani Cheeneebash
Faculty of Science, University of Mauritius, Réduit, Mauritius
Ashvin Gopaul
Faculty of Science, University of Mauritius, Réduit, Mauritius
Smita S.D. Goorah
Faculty of Science, University of Mauritius, Réduit, Mauritius
The aim of the study is to use artificial intelligence tools as a clinical decision support in assessing cardiovascular risk in patients. A two-layer neural network using the Levenberg-Marquardt algorithm and the resilient backpropagation have been used in the proposed artificial neural network. It has been shown how this network is efficient in predicting cardiovascular risk in individual patients by using the Long Beach dataset. The use of this new network seems to better address the prediction of cardiovascular disease at an individual level.
PDF Fulltext XML References Citation

How to cite this article

Beatrice Fidele, Jayrani Cheeneebash, Ashvin Gopaul and Smita S.D. Goorah, 2009. Artificial Neural Network as a Clinical Decision-Supporting Tool to Predict Cardiovascular Disease. Trends in Applied Sciences Research, 4: 36-46.

URL: https://scialert.net/abstract/?doi=tasr.2009.36.46

Leave a Comment

Your email address will not be published. Required fields are marked *

Article Trend

Total views 3710

References

  1. Assmann, G., P. Cullen and H. Schulte, 2002. Simple scoring scheme for calculating the risk of acute coronary events based on the 10 year follow up of the prospective cardiovascular Munster (PROCAM) study. Circulation, 105: 310-315.
    Direct Link
  2. Conroy, R.M., K. Pyorala, A.P. Fitzgerald, S. Sans and A. Menotti, 2003. SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur. Heart J., 24: 987-1003.
    CrossRefDirect Link
  3. Enzo, G., 2006. How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: Problems with the current methods. BMC Cardiovas. Disorders, 6: 20.
    CrossRefDirect Link
  4. Lourakis, M.I.A., 2005. A brief description of the levenberg-Marquardt algorithm implemented by levmar. http://www.ics.forth.gr/~lourakis/levmar/levmar.pdf.
  5. Menotti, A., P.E. Puddu and M. Lanti, 2000. Comparison of the framingham risk function-based coronary chart with risk function from an Italian population study. Eur. Heart J., 21: 365-370.
    CrossRef
  6. Menotti, A., P.E. Puddu and M. Lanti, 2002. The estimate of cardiovascular risk. Theory, tools and problems. Ann. Ital. Med. Int., 17: 81-94.
    PubMed
  7. Riedmiller, M., 1994. Rprop-description and implementation. http://citeseer.ist.psu.edu/riedmiller94rprop.html.
  8. Wilson, P.W., R.B. D'Agostino, D. Levy, A.M. Belanger, H. Silbershatz and W.B. Kannel, 1998. Prediction of coronary heart disease using risk factor categories. Circulation, 97: 1837-1847.
    CrossRefPubMedDirect Link

Keywords