Abstract
Objective
Genetic programming is a search method that can be used
to solve complex associations between large numbers of
variables. It has been used, for example, for
myoelectrical signal recognition, but its value for
medical prediction as in diagnostic and prognostic
settings, has not been documented.
Study design and setting
We compared genetic programming and the commonly used
logistic regression technique in the development of a
prediction model using empirical data from a study on
diagnosis of pulmonary embolism. Using part (67%) of
the data, we developed and internally validated (using
bootstrapping techniques) a diagnostic prediction model
by genetic programming and by logistic regression, and
compared both on their predictive ability in the
remaining data (validation set).
Results
In the validation set, the area under the ROC curve of
the genetic programming model was significantly larger
(0.73; 95%CI: 0.64-0.82) than that of the logistic
regression model (0.68; 0.59-0.77). The calibration of
both models was similar, indicating a similar amount of
overoptimism.
Conclusion
Although the interpretation of a genetic programming
model is less intuitive and this is the first empirical
study quantifying its value for medical prediction,
genetic programming seems a promising technique to
develop prediction rules for diagnostic and prognostic
purposes.
Users
Please
log in to take part in the discussion (add own reviews or comments).