Risk factors for coronary artery disease and the use of neural networks to predict the presence or absence of XXXX .

@entity101 @entity65
BACKGROUND: The Framingham Heart Study was initiated in 1948 as a long-term longitudinal study to identify risk factors associated with @entity65 ( @entity65 ). Over the years the scope of the study has expanded to include offspring and other family members of the original cohort, marker data useful for gene mapping and information on other diseases. As a result, it is a rich resource for many areas of research going beyond the original goals. As part of the Genetic Analysis Workshop 13, we used data from the study to evaluate the ability of neural networks to use @entity65 risk factors as training data for predictions of normal and @entity101 . RESULTS: Applying two different strategies to the coding of @entity65 risk data as risk factors (one longitudinal and one independent of time), we found that neural networks could not be trained to clearly separate individuals into normal and @entity101 groups. When training was successful, validation was not, suggesting over-fitting of the model. When the number of parameters was reduced, training was not as good. An analysis of the input data showed that the neural networks were, in fact, finding consistent patterns, but that these patterns were not correlated with the presence or absence of @entity101 . CONCLUSION: Neural network analysis, applied to risk factors for @entity65 in the Framingham data, did not lead to a clear classification of individuals into groups with normal and @entity101 . Thus, although @entity101 may itself be a risk factor for @entity65 , it does not appear to be clearly predictable using observations from a set of other @entity65 risk factors.
