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International Journal of Epidemiology 2002;31:1262-1264
© International Epidemiological Association 2002

Theory and Methods

Commentary: Improved coronary risk prediction using neural networks

Margaret May

University of Bristol, Department of Social Medicine, Canynge Hall, Whiteladies Road, Bristol BS8 2PR, UK.

The first 10% of the full text of this article appears below.

Coronary heart disease (CHD) remains the major cause of premature death in developed countries. Many of the risk factors for CHD are well known and interventions exist for some of them, e.g. statins for reducing cholesterol and drugs for hypertension. In the paper by Reinhard Voss and colleagues, a new approach for predicting those at high absolute risk (>20%) of a coronary event in the next 10 years using neural network models is compared to a method using a standard logistic regression model.1 The success of the multi-layer perceptron neural network (MLP-NN) is quite remarkable: 74.5% of coronary events were predicted compared to 45.8% by the logistic regression model. Furthermore, this was achieved by predicting a smaller number of men to be at high risk (7.9%) compared to the logistic regression model (8.4%) so that less men . . . [Full Text of this Article]


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