International Journal of Epidemiology 2002;31:1253-1262
© International Epidemiological Association 2002
Theory and Methods |
Prediction of risk of coronary events in middle-aged men in the Prospective Cardiovascular Münster Study (PROCAM) using neural networks
a Institute of Clinical Chemistry and Laboratory Medicine,
b Institute of Arteriosclerosis Research, University of Münster, Germany.
Correspondence: Dr Paul Cullen, Institut für Arterioskleroseforschung an der Universität Münster, Domagkstraße 3, 48149 Münster, Germany. E-mail: cullen{at}uni-muenster.de
Background Logistic regression (LR) is commonly used to estimate risk of coronary heart disease. We investigated if neural networks improved on the risk estimate of LR by analysing data from the Prospective Cardiovascular Münster Study (PROCAM), a large prospective epidemiological study of risk factors for coronary heart disease among men and women at work in northern Germany.
Methods We used a multi-layer perceptron (MLP) and probabilistic neural networks (PNN) to estimate the risk of myocardial infarction or acute coronary death (coronary events) during 10 years’ follow-up among 5159 men aged 35–65 years at recruitment into PROCAM. In all, 325 coronary events occurred in this group. We assessed the performance of each procedure by measuring the area under the receiver-operating characteristics curve (AUROC).
Results The AUROC of the MLP was greater than that of the PNN (0.897 versus 0.872), and both exceeded the AUROC for LR of 0.840. If ‘high risk’ is defined as an event risk >20% in 10 years, LR classified 8.4% of men as high risk, 36.7% of whom suffered an event in 10 years (45.8% of all events). The MLP classified 7.9% as high risk, 64.0% of whom suffered an event (74.5% of all events), while with the PNN, only 3.9% were at high risk, 58.6% of whom suffered an event (33.5% of all events).
Conclusion Intervention trials indicate that about one in three coronary events can be prevented by 5 years of lipid-lowering treatment. Our analysis suggests that use of the MLP to identify high-risk individuals as candidates for drug treatment would allow prevention of 25% of coronary events in middle-aged men, compared to 15% and 11% with LR and the PNN, respectively.
Keywords Coronary heart disease, risk factors, neural networks, logistic regression
Accepted 31 July 2002
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  M. T. Cooney, A. L. Dudina, and I. M. Graham Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians. J. Am. Coll. Cardiol., September 29, 2009; 54(14): 1209 - 1227. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. P Hearty and M. J Gibney Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees Am. J. Clinical Nutrition, December 1, 2008; 88(6): 1632 - 1642. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. M Ridker, J. E. Buring, N. Rifai, and N. R. Cook Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women: The Reynolds Risk Score JAMA, February 14, 2007; 297(6): 611 - 619. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Assmann Dyslipidaemia and global cardiovascular risk: clinical issues Eur. Heart J. Suppl., October 1, 2006; 8(suppl_F): F40 - F46. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. T. Donnan, L. Donnelly, J. P. New, and A. D. Morris Derivation and Validation of a Prediction Score for Major Coronary Heart Disease Events in a U.K. Type 2 Diabetic Population. Diabetes Care, June 1, 2006; 29(6): 1231 - 1236. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. S. Vasan Biomarkers of Cardiovascular Disease: Molecular Basis and Practical Considerations Circulation, May 16, 2006; 113(19): 2335 - 2362. [Full Text] [PDF]
|
|
|
|
 |
|
 C. Konrad, C. Langer, G.A. Muller, K. Berger, R. Dziewas, F. Stogbauer, D.G. Nabavi, R. Junker, E.B. Ringelstein, and G. Kuhlenbaumer Protease Inhibitors in Spontaneous Cervical Artery Dissections Stroke, January 1, 2005; 36(1): 9 - 13. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. S. Ford, W. H. Giles, and A. H. Mokdad The distribution of 10-Year risk for coronary heart disease among U.S. adults: Findings from the National Health and Nutrition Examination Survey III J. Am. Coll. Cardiol., May 19, 2004; 43(10): 1791 - 1796. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. von Eckardstein Is there a need for novel cardiovascular risk factors? Nephrol. Dial. Transplant., April 1, 2004; 19(4): 761 - 765. [Full Text] [PDF]
|
|
|
|
 |
|
 H.-W. Hense Observations, predictions and decisions--assessing cardiovascular risk assessment Int. J. Epidemiol., April 1, 2004; 33(2): 235 - 239. [Full Text] [PDF]
|
|
|
|
 |
|
 S. E. Humphries, P. M. Ridker, and P. J. Talmud Genetic Testing for Cardiovascular Disease Susceptibility: A Useful Clinical Management Tool or Possible Misinformation? Arterioscler Thromb Vasc Biol, April 1, 2004; 24(4): 628 - 636. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Naghavi, P. Libby, E. Falk, S. W. Casscells, S. Litovsky, J. Rumberger, J. J. Badimon, C. Stefanadis, P. Moreno, G. Pasterkamp, et al. From Vulnerable Plaque to Vulnerable Patient: A Call for New Definitions and Risk Assessment Strategies: Part II Circulation, October 14, 2003; 108(15): 1772 - 1778. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. May Commentary: Improved coronary risk prediction using neural networks Int. J. Epidemiol., December 1, 2002; 31(6): 1262 - 1264. [Full Text]
|
|